Inquiry, Measurement & Models — Print · Science 6: Matter, Energy & Earth Systems, Grade 6
What makes a scientific explanation traceable and testable? Under one defined mass-and-draining method, which equal-sized towel sample retains more water?
Printable chapter lessons and review.
Scientific Inquiry: Questions, Evidence & Explanations
Learning goals
- I can tell an investigable question from one science cannot settle.
- I can separate an observation from an inference drawn out of it.
- I can explain why an explanation has to say what would count against it.
Readings for this unit
Before this unit
Read the chapter opening and the named observation or diagram. Clarify one word or unit that blocks understanding. Recall the relevant earlier measurement, matter, or energy idea.
Study the unit across three 35–45-minute class sessions. Use the worked examples after an attempt and the supplied records when actual equipment is not available.
In 1847 a doctor in Vienna named Ignaz Semmelweis noticed something that should not have been true. In one maternity ward of his hospital, run by doctors, about 18 in every 100 mothers died of childbed fever. In the next ward, run by midwives, the figure was about 2 in 100. Same building, same city, same year.
He did not start by guessing a cause. He started by listing differences between the wards and testing them one at a time, and he found one that mattered: the doctors came straight from dissecting bodies, and the midwives did not. When he made them wash their hands in a chlorine solution, deaths in his ward fell to around 1 in 100 within months.
That is the whole shape of scientific work in one story. An observation nobody can deny, a list of possible causes, a test that could have come out either way, and a measurement at the end. Notice too that he was ignored for twenty years, which is part of the story as well.
Build your understanding
Where this fits
This is the first lesson of the course and the one the others lean on. Everything that follows -- particles, circuits, plate boundaries, moon phases -- is an explanation somebody built out of evidence, and knowing how that is done is what lets you judge whether to believe it. The chapter after this covers how to measure the evidence.
Use three flexible 40-45-minute sessions: what makes a question investigable; observation against inference; building an explanation that can be tested, then the practice set, revision and the exit check. Workshop, review, quiz and exams have separate time.
Part 1 · Which questions science can answer
Science can settle a question only when some possible result would count against the answer. “Does a heavier ball fall faster than a light one?” is answerable: drop a 1 kg ball and a 5 kg ball from the same height and time them. They land together, which is not what most people expect, and that result would have refuted the opposite claim. “Is this piece of music beautiful?” is not answerable this way, not because the question is silly but because no measurement could decide it. Many real questions sit in between and can be made answerable by sharpening them. “Is this water safe?” cannot be measured as written, but “does this water contain more than 10 mg of nitrate per litre?” can be, because a test either exceeds that number or it does not. Turning a vague question into a measurable one is most of the work of planning an investigation.
The test to apply is always the same, and you can use it on anything you read. Ask what result would make the person making the claim change their mind. If they can name one, you are looking at a scientific claim, and you can go and check. If no possible evidence would shift them, the claim may still be true or important, but it is not something science has the tools to settle, and dressing it up in scientific language does not change that. This matters well outside a laboratory. Advertisements, headlines and arguments online all borrow the look of scientific claims, and the quickest way to tell the difference is to ask what evidence the person would accept against themselves. A claim with an answer ready for every possible result is not a strong claim; it has stopped being about evidence at all.
Pause and explain
What makes a question investigable?
Check your reasoning after answering
That some possible result would count against the answer. If you can name a measurement whose outcome would make you abandon the claim, the claim can be tested and checked by anyone else. If no result would change your mind, there is nothing for evidence to do, however reasonable the idea sounds.
Part 2 · What you saw and what you worked out
An observation is what reaches your senses or your instrument. An inference is the explanation you build on top of it. “The thermometer in the sun reads 31 °C and the one in the shade reads 24 °C” is an observation. “Sunlight heats the air” is an inference, and in this case a wrong one: sunlight is warming the thermometer itself, not the air around it, which is exactly why weather stations keep thermometers in a shaded, ventilated box. Instruments do not settle the problem by themselves, because an instrument always reports something narrower than you assume. A thermometer reports its own temperature, a light sensor reports the light reaching its surface, and a balance reports the force pushing down on it. Knowing exactly what a device measures is what tells you which inferences its reading can support.
Keeping the two apart is the single most useful habit in this course, because almost every mistake you will meet is an inference being reported as though it were an observation. When you write up an investigation, put what you measured in one column and what you concluded in another. Anyone can check the first column. The second is where you can be argued with, and where a different explanation of the same numbers might turn out to fit better. A useful exercise is to take any conclusion from a textbook and hunt for the observation underneath it. Sometimes it is one measurement; far more often it is thousands, taken by different people with different instruments over decades. That gap between a one-line statement and the evidence behind it is where most of science actually lives.
Pause and explain
Why should an observation be recorded separately from an inference?
Check your reasoning after answering
Because they can be checked in different ways. Another person can repeat a measurement and get the same reading, so the record stands on its own. The explanation built on top of it may be wrong even when the reading is right, as with a thermometer in sunlight, and keeping it separate makes it possible to replace the explanation without discarding the data.
Part 3 · Evidence, and how much of it
One result is a start, not a finding. Semmelweis did not change hospital practice on the strength of one week; he tracked monthly death rates across years, which is why the drop from around 18 per 100 to about 1 per 100 was convincing rather than a run of luck. Numbers bounce around on their own, and the job of repeating a measurement is to find out how much of the change you are seeing is real. Repeating also gives you something no single reading can: the spread. Measure the same length five times and get 64, 65, 65, 66 and 65 mm, and that spread of about 2 mm is your honest uncertainty -- a difference smaller than it between two objects is not a difference you have actually detected. One measurement hands you a number with no way to judge it.
The amount of evidence a claim needs depends on what it would overturn. A claim that fits everything already known can rest on a modest result. A claim that would overturn a well-tested explanation needs evidence strong enough to also explain why everyone measuring before got a different answer. This is not stubbornness: a well-tested idea is one that has already survived many chances to fail, and that survival is itself evidence that has to be accounted for. There is a fair version of the opposite worry. Sometimes a well-tested idea is wrong and the evidence against it is brushed aside for far too long, which is exactly what happened here: Semmelweis's figures were sound and his explanation was right, and he was rejected for two decades partly because nobody had yet described germs. The rule is to weigh evidence, not to defend whatever is established.
Pause and explain
Why does a surprising claim need more evidence than an ordinary one?
Check your reasoning after answering
Because it has to account for the earlier measurements as well as its own. An idea that is already well tested has survived many opportunities to be shown wrong, and that record counts as evidence for it. Overturning it means explaining both your new result and why everyone who measured before found something different.
Part 4 · An explanation that could be wrong
A scientific explanation does three things: it accounts for the evidence you have, it says what mechanism produced it, and it makes a prediction about something you have not measured yet. Semmelweis's explanation had all three -- the doctors carried something from the dissection room, washing removed it, and therefore wards where nobody dissected should already have low rates. The midwives' ward was exactly that prediction, and it held. Compare that with an explanation that cannot fail. If someone says the deaths were caused by bad air, and then explains every low-death ward by saying its air happened to be better, nothing has been predicted and no result could ever count against it. Bad air was the accepted explanation at the time, which shows that being widely held is not the same as being testable.
The last part is what separates an explanation from a story that fits. Almost any set of facts can be explained after the event by something; the test is whether the explanation commits you to a claim about the next measurement. When you write your own explanations in this course, finish each one with the sentence “if this is right, then we should also find...”, and then go and look. Being wrong at that point is useful, which is a strange idea at first and the most important one here. Keep one more thing in mind while you do it. An explanation that turns out wrong is not wasted work provided the prediction was clear, because you now know something about the world you did not know before, and the next explanation has to account for it. Most of what is in this course was reached by somebody being specifically, usefully wrong first.
Pause and explain
Why must a scientific explanation predict something not yet measured?
Check your reasoning after answering
Because an explanation invented after the fact can always be made to fit what already happened. Committing to a result you have not seen gives the idea a genuine chance to fail, and surviving that is what makes it worth believing. An account that explains everything already known but predicts nothing new cannot be tested at all.
Apply the whole unit · Sort the claims
Three statements: (a) adding 10 g of salt to 100 mL of water raises its boiling point; (b) the blue mug keeps coffee warmer than the white one; (c) blue is a calming colour.
Your task
Decide which can be settled by measurement, say how, and explain the one that cannot.
What your response should show
- Names a specific result that would falsify each testable claim
- Describes a measurement with a quantity and a comparison
- Explains the third case by the claim's wording, not by dismissing it
- Keeps observations separate from the conclusions drawn from them
Response guidance — after your attempt
For each statement, name a result that would show it is false.
Describe the measurement you would take for the two that can be tested.
Explain what makes the third different, without calling it unimportant.
Statement (a) is investigable: boil 100 mL of pure water, record the temperature, repeat with 10 g of salt dissolved, and compare. A result of no change, or a lower temperature, would refute it; in fact the boiling point rises by roughly 1 °C. Statement (b) is investigable: fill both mugs with water at the same starting temperature, say 80 °C, leave them in the same room, and measure both after 20 minutes. If the blue mug is cooler, the claim fails. Statement (c) is different. People's reported feelings can be surveyed, and you could measure heart rate in a blue room against a white one, but the claim as written does not say what counts as calming, so no result would settle it until the word is defined. That is a limit of the question's wording rather than a judgement about whether colour matters to people.
Scientific references for this unit
- Royal Society: what makes a question answerable by experiment
- US National Institutes of Health: Semmelweis and the origins of handwashing
- National Oceanic and Atmospheric Administration: how weather stations shield thermometers
- Understanding Science, University of California Museum of Paleontology
Parent / teacher teaching notes
Begin with the literal observation, quantity, or diagram relationship. Ask the learner to explain why the evidence supports the claim, check a relevant unit or boundary, then discuss an alternative. The supplied numbers are illustrative, not real observations. A parent can read aloud and allow a spoken rehearsal before writing. Judge science and evidence rather than political, faith, or family agreement. Use the complete data option whenever an actual procedure is unavailable or unsuitable.
The key idea
Science answers one narrow kind of question: the kind where you can collect evidence that would change your mind. The question is not whether an idea sounds sensible but whether there is a measurement that could show it is wrong.
Worked examples
Example 1 · Two explanations for a puddle
- Identify mass lost and the stated hour.
- Name both changed conditions.
- Propose a matched comparison and a limited claim.
Example 2 · Transfer to a different case
- Identify the distinction and conditions in this new case.
- Use the relevant mechanism or quantity; preserve units and the chosen boundary.
- Explain why the answer fits and what further evidence would be needed.
Example 3 · Ask a question that evidence can address · a focused example
- State the literal condition or observation in the example.
- Apply the distinction explained in this teaching part.
- Check whether the claim stays within the given evidence.
Avoid these mistakes
- Misleading claim: A plausible explanation is automatically a directly observed fact.
- Misleading claim: Every scientific investigation must follow exactly the same steps.
- Omitting units, measurement timing, or a model boundary makes the evidence hard to interpret.
- Treating illustrative classroom data as real field measurements or copying a model is not an independent explanation.
Check your understanding
Does a yellow leaf prove lack of water?
Discuss the answer
No. The color is an observation; the proposed cause needs additional evidence that distinguishes it from other explanations.
Why is the first towel question clearer?
Discuss the answer
It defines the measured outcome, time, and piece size. The result concerns that absorption test, not every possible meaning of best.
What reasoning connects mass gain with absorption?
Discuss the answer
Water retained in the towel adds mass. Comparable dry-to-wet measurements can therefore compare retained water, while spills and timing must be checked.
Can one absorption result rank every towel property?
Discuss the answer
No. The result concerns one measured property under a stated method; cost, durability, and waste need their own evidence.
Sustained scientific explanation and revision
These activities are scheduled in the parent plan. Save a first response, a correction, and the reason for the change in the science notebook.
Activity 1 · Scientific Inquiry: Questions, Evidence & Explanations · Model and explain
Read A Neat Result Is Not Yet an Explanation and the linked unit teaching; use its complete supplied case and diagrams.
Your task
For Scientific Inquiry: Questions, Evidence & Explanations, study Parts 3–4 and attempt the worked case: Explain what the measurements show and why sunlight alone is not established as the cause. Explain the steps before opening the model.
Success criteria
- An accurate scientific claim answering the actual question.
- Relevant evidence or a labeled model with quantities and units when applicable.
- Reasoning that connects evidence with the mechanism rather than only repeats it.
- A specific method, boundary, scale, or alternative-explanation limit.
Hint
Begin with the actual observation, quantity, or diagram relationship. Explain how it bears on the question before making a broad claim.
Response guidance — discuss after your attempt
The sunny tray lost 4 g more in the hour, but sunlight and surface area both differ. Greater exposed area could also affect evaporation. Use identical trays with equal water amounts and matched timing, varying only the intended exposure. The present evidence describes a difference without isolating its cause.
Activity 2 · Scientific Inquiry: Questions, Evidence & Explanations · Practice, revise and exit
Read A Neat Result Is Not Yet an Explanation and the linked unit teaching; use its complete supplied case and diagrams.
Your task
For Scientific Inquiry: Questions, Evidence & Explanations, complete practice questions 1–6 and both choice checks, then answer independently: A photograph shows a wet pavement. Does it establish that rain caused the water? Save a correction explaining one changed claim or method.
Success criteria
- An accurate scientific claim answering the actual question.
- Relevant evidence or a labeled model with quantities and units when applicable.
- Reasoning that connects evidence with the mechanism rather than only repeats it.
- A specific method, boundary, scale, or alternative-explanation limit.
Hint
Begin with the actual observation, quantity, or diagram relationship. Explain how it bears on the question before making a broad claim.
Response guidance — discuss after your attempt
No. It shows water at that moment; washing, irrigation, or another source could be involved. A cause needs additional information.
Words to know
- observation
- A recorded detection or measurement under a stated method.
- inference
- A reasoned interpretation of observations rather than the observation itself.
- evidence
- A relevant observation, measurement, or record used to evaluate a claim.
- system boundary
- The chosen limit specifying what is included and which exchanges cross it.
- uncertainty
- A stated limit in measurement or knowledge, distinct from a careless mistake.
In the real world
A doctor uses the exact same loop you do: she observes your symptoms, forms a hypothesis (a possible diagnosis), tests it with a lab test or scan, analyzes the results, and concludes what is wrong — then adjusts if the evidence points somewhere new.
Exit check
A photograph shows a wet pavement. Does it establish that rain caused the water?
Check your explanation after answering
No. It shows water at that moment; washing, irrigation, or another source could be involved. A cause needs additional information.
Explain your reasoning without notes. If a key idea is missing, revisit the lesson and try again.
Variables, Controls & Repeated Trials
Learning goals
- I can name the independent, dependent and controlled variables in an investigation.
- I can explain why a control group is needed even when the treatment obviously works.
- I can explain what repeating a trial tells you that one trial cannot.
Readings for this unit
Before this unit
Read the chapter opening and the named observation or diagram. Clarify one word or unit that blocks understanding. Recall the relevant earlier measurement, matter, or energy idea.
Study the unit across three 35–45-minute class sessions. Use the worked examples after an attempt and the supplied records when actual equipment is not available.
In 1954 the United States ran the largest medical trial ever attempted up to that point. Polio was paralysing tens of thousands of children a year, a vaccine existed, and the question was whether it worked. About 1.8 million children took part, and in the strictest part of the trial 623,972 were split at random into two groups.
One group received the vaccine. The other received an injection of salt water that looked identical, and neither the children nor the doctors giving the injections knew which was which. At the end, paralytic polio occurred in about 16 children per 100,000 in the vaccinated group and about 57 per 100,000 in the group given salt water.
Every element of that design exists for a reason you will use in far smaller investigations. One thing changed, everything else held steady, a comparison group that got nothing active, and enough children that a difference this size could not be chance.
Build your understanding
Where this fits
The last lesson was about what makes a question answerable. This one is about building the test once you have the question, and it introduces the vocabulary used in every investigation in this course: independent, dependent and controlled variables, control groups and repeated trials. The chapter investigation will ask you to design one of your own.
Use three flexible 40-45-minute sessions: the three kinds of variable; control groups and why they are needed; repeated trials and confounding variables, then the practice set, revision and the exit check. Workshop, review, quiz and exams have separate time.
Part 1 · One thing changed, everything else held
Every investigation has three kinds of variable and it is worth being strict about the words. The independent variable is the one thing you deliberately change -- the amount of light, the mass hung on a spring, the temperature of the water. The dependent variable is what you measure to see what happened: the height of the seedling in millimetres, the stretch of the spring in centimetres, the time to dissolve in seconds. The controlled variables are everything you hold steady on purpose, and there are usually many more of them than students expect, which is why listing them is part of the plan rather than an afterthought.
Take a simple case. You want to know whether seedlings grow taller with more light, so you put one tray under a lamp for 12 hours a day and another for 4 hours. To make that a fair test the trays need the same soil, the same seeds from the same packet, the same 50 mL of water each day, the same room temperature and the same size pots. If the bright tray also sits on a warm windowsill, you have changed two things at once, and when it grows 30 mm taller you have no way of saying which change did it. The result is not wrong, it is simply unusable. A good habit is to write the controlled variables down as a numbered list before you touch anything, because the ones you forget are never the obvious ones.
Pause and explain
What goes wrong if two variables change at the same time?
Check your reasoning after answering
The result stops being attributable. If the brighter tray is also the warmer one, a 30 mm difference in growth could have come from either factor or from both together, and nothing in the data separates them. The measurement itself may be perfectly accurate; what has been lost is any ability to say what caused the difference.
Part 2 · The group you did nothing to
A control group is the comparison you set up by deliberately not applying the treatment. It sounds wasteful and it is the most important part of the design, because many things change on their own. Polio case numbers swung widely from year to year before any vaccine existed, so a vaccinated group with few cases would have proved nothing without a group measured over the same months in the same towns. The 57 per 100,000 figure is what tells you that 16 per 100,000 is low.
Two refinements in the 1954 trial are worth copying. The comparison group received a placebo, an injection of salt water indistinguishable from the real one, so that both groups were treated the same in every respect except the vaccine itself. And the trial was double-blind: neither the child nor the doctor diagnosing them knew which injection had been given. That matters because a doctor who knows a child was vaccinated may, without meaning to, record a borderline case differently, and the decision about whether a case counts is exactly where expectation leaks in. The same leak appears in school investigations. If you know which tray is supposed to grow faster, you will measure that tray slightly more generously without ever deciding to, which is why it helps to label trays A and B and have someone else hold the key until the measuring is finished.
Pause and explain
Why was the comparison group given an injection of salt water rather than nothing at all?
Check your reasoning after answering
So that the two groups differed only in the vaccine. Being injected is itself an experience, and children who knew they had received nothing could have behaved differently or been assessed differently. Matching everything except the active ingredient means any difference in outcome has only one candidate cause left.
Part 3 · Doing it again
A single trial gives you a number with no way of judging it. Repeat the measurement and you learn the spread, which is what tells you whether a difference is real. If five measurements of a seedling give 62, 65, 64, 66 and 63 mm, the readings scatter over about 4 mm, and a 2 mm difference between two trays is smaller than your own noise. The same comparison with a 30 mm difference is on completely different footing, and you only know which situation you are in because you repeated.
Repeating also exposes the occasional reading that does not belong -- a seedling that was knocked over, a timer started late. The honest response is to record it, say what happened, and explain why you excluded it, rather than quietly dropping it because it spoiled the pattern. In the polio trial the equivalent protection was sheer size: with 623,972 children, a run of bad luck large enough to produce a gap between 16 and 57 per 100,000 is so unlikely that it can be set aside, which is what large numbers buy you. There is a limit worth knowing: repeating a measurement reduces random scatter but does nothing at all about a fault that pushes every reading the same way. A ruler with a worn end gives you five consistent, consistently wrong numbers, and no amount of repetition reveals it. That is the subject of the next lesson.
Pause and explain
What does repeating a measurement tell you that a single reading cannot?
Check your reasoning after answering
How much the number moves on its own. Five readings that scatter across 4 mm establish that any difference smaller than that is within ordinary variation and has not really been detected. One reading supplies a value with no indication of its reliability, so there is no way to judge whether a comparison means anything.
Part 4 · The variable nobody listed
A confounding variable is one that changes alongside your independent variable without being on your list, and it is the commonest reason an investigation reaches a wrong conclusion. Drowning deaths and ice cream sales both rise in July; neither causes the other, and the hidden variable is hot weather putting more people in the water and more people at the ice cream counter. Spotting the pattern is easy once it is pointed out, and genuinely hard in the middle of your own investigation.
The defence is to list controlled variables before you start, and then to ask what else might differ between your groups that you have not written down. Where you cannot hold something steady, randomising helps: the polio trial assigned children to groups by chance precisely so that unlisted differences -- diet, neighbourhood, how often they swam in public pools -- would be spread roughly evenly between the groups instead of piling up in one. Randomising does not remove confounding variables, it stops them lining up with the treatment. For a classroom investigation the same principle is easy to apply. Rather than putting the first ten seedlings in the bright tray and the rest in the dim one, shuffle them and deal them out, because the first ten out of the packet may well be the largest seeds and you would never know.
Pause and explain
How does assigning subjects at random help when you cannot control every variable?
Check your reasoning after answering
It stops unlisted differences from lining up with the treatment. Diet, neighbourhood and habits cannot all be held steady, but if people are allocated by chance those factors should appear in roughly equal measure in both groups, so they no longer offer an alternative explanation for a difference between them.
Apply the whole unit · Design a fair test
A claim says that adding 5 g of sugar to the water in a vase makes cut flowers last longer. You have 20 identical flowers, 10 vases, water, sugar and a ruler.
Your task
Design an investigation that could show the claim is false, and say how you would judge the result.
What your response should show
- Names all three kinds of variable and makes the dependent one countable
- Includes a control group and explains why the comparison is necessary
- Specifies the number of repeats and uses the spread to judge the result
- Lists controlled variables with quantities rather than in general terms
Response guidance — after your attempt
Name the independent, dependent and controlled variables, and say how you will measure the dependent one.
Describe your control group and explain why it is needed here.
State how many times you will repeat and what size of difference you would accept as real.
The independent variable is whether 5 g of sugar is added; the dependent variable is how long the flowers last, measured as the number of days until the stem bends past horizontal, since “last longer” has to be turned into something countable. Controlled variables: the same species and size of flower, 200 mL of water in every vase, the same room at about 20 °C, the same light, stems cut to the same length on the same day, and the water changed on the same schedule. The control group is five vases with plain water, needed because flowers wilt at different rates anyway and without that comparison a six-day average means nothing. I would use five vases in each group, calculate the average for each, and look at the spread within each group first: if individual flowers in the plain-water group vary by three days, then a one-day difference between the group averages is inside the noise and I have shown nothing. A difference of four or more days, with tight spread, would be worth taking seriously.
Scientific references for this unit
- US National Library of Medicine: the 1954 Salk polio vaccine field trial
- Centers for Disease Control and Prevention: polio history and vaccine data
- Understanding Science, University of California Museum of Paleontology: fair tests
- Royal Statistical Society: why randomisation and control groups matter
Parent / teacher teaching notes
Begin with the literal observation, quantity, or diagram relationship. Ask the learner to explain why the evidence supports the claim, check a relevant unit or boundary, then discuss an alternative. The supplied numbers are illustrative, not real observations. A parent can read aloud and allow a spoken rehearsal before writing. Judge science and evidence rather than political, faith, or family agreement. Use the complete data option whenever an actual procedure is unavailable or unsuitable.
The key idea
A fair test changes one thing and holds everything else steady, and compares the result against a group you deliberately did nothing to. Without that second group you cannot tell whether your change caused the difference or whether it would have happened anyway.
Worked examples
Example 1 · A fair bridge comparison
- Identify both geometric differences.
- Explain how another changed factor could affect load.
- Specify matched conditions and repeated fresh samples.
Example 2 · Transfer to a different case
- Identify the distinction and conditions in this new case.
- Use the relevant mechanism or quantity; preserve units and the chosen boundary.
- Explain why the answer fits and what further evidence would be needed.
Example 3 · Keep other important conditions comparable · a focused example
- State the literal condition or observation in the example.
- Apply the distinction explained in this teaching part.
- Check whether the claim stays within the given evidence.
Avoid these mistakes
- Misleading claim: Repeating a confounded test makes its causal claim valid.
- Misleading claim: A control treatment automatically keeps every other condition equal.
- Omitting units, measurement timing, or a model boundary makes the evidence hard to interpret.
- Treating illustrative classroom data as real field measurements or copying a model is not an independent explanation.
Check your understanding
Which variable is the outcome?
Discuss the answer
The number of identical counters held before collapse, using the stated placement and failure rule.
What two changes confound this comparison?
Discuss the answer
Strip width and material or thickness. Use the same material and thickness while varying width if width is the intended factor.
Why keep the count of 8 rather than only 10?
Discuss the answer
Keeping all planned trials represents the observed variation and prevents selecting only the result that favors a claim.
Does an unwrapped reference guarantee a fair test?
Discuss the answer
No. It supplies a reference treatment, but the other influential conditions and measurement procedure must still be comparable.
Sustained scientific explanation and revision
These activities are scheduled in the parent plan. Save a first response, a correction, and the reason for the change in the science notebook.
Activity 1 · Variables, Controls & Repeated Trials · Model and explain
Read A Neat Result Is Not Yet an Explanation and the linked unit teaching; use its complete supplied case and diagrams.
Your task
For Variables, Controls & Repeated Trials, study Parts 3–4 and attempt the worked case: Explain why the comparison does not isolate width and propose one improvement. Explain the steps before opening the model.
Success criteria
- An accurate scientific claim answering the actual question.
- Relevant evidence or a labeled model with quantities and units when applicable.
- Reasoning that connects evidence with the mechanism rather than only repeats it.
- A specific method, boundary, scale, or alternative-explanation limit.
Hint
Begin with the actual observation, quantity, or diagram relationship. Explain how it bears on the question before making a broad claim.
Response guidance — discuss after your attempt
Width and length both change. Length can affect bending, so the larger load does not isolate width. Keep length, material, support spacing, and counter placement the same, vary width, and use several fresh strips for each treatment. Compare all recorded loads under the same failure rule.
Activity 2 · Variables, Controls & Repeated Trials · Practice, revise and exit
Read A Neat Result Is Not Yet an Explanation and the linked unit teaching; use its complete supplied case and diagrams.
Your task
For Variables, Controls & Repeated Trials, complete practice questions 1–6 and both choice checks, then answer independently: Three repeated trials all use different counter sizes. Does repetition fix the method? Save a correction explaining one changed claim or method.
Success criteria
- An accurate scientific claim answering the actual question.
- Relevant evidence or a labeled model with quantities and units when applicable.
- Reasoning that connects evidence with the mechanism rather than only repeats it.
- A specific method, boundary, scale, or alternative-explanation limit.
Hint
Begin with the actual observation, quantity, or diagram relationship. Explain how it bears on the question before making a broad claim.
Response guidance — discuss after your attempt
No. Counter count is not comparable when counter mass differs. Use identical counters or measure load mass, then repeat the corrected method.
Words to know
- independent variable
- The factor deliberately changed for the intended comparison.
- dependent variable
- The response measured under the stated method.
- evidence
- A relevant observation, measurement, or record used to evaluate a claim.
- system boundary
- The chosen limit specifying what is included and which exchanges cross it.
- uncertainty
- A stated limit in measurement or knowledge, distinct from a careless mistake.
In the real world
Before a new medicine is approved, it is tested in a clinical trial where one group gets the real drug and a control group gets a look-alike placebo with no medicine — everything else kept the same. Comparing the two groups is the only way to know whether the drug itself actually works.
Exit check
Three repeated trials all use different counter sizes. Does repetition fix the method?
Check your explanation after answering
No. Counter count is not comparable when counter mass differs. Use identical counters or measure load mass, then repeat the corrected method.
Explain your reasoning without notes. If a key idea is missing, revisit the lesson and try again.
Metric Measurement, Units & Uncertainty
Learning goals
- I can convert between metric units using the prefixes milli, centi and kilo.
- I can read an instrument to its smallest division plus one estimated digit.
- I can explain why two measurements that differ may not be measuring different things.
Readings for this unit
Before this unit
Read the chapter opening and the named observation or diagram. Clarify one word or unit that blocks understanding. Recall the relevant earlier measurement, matter, or energy idea.
Study the unit across three 35–45-minute class sessions. Use the worked examples after an attempt and the supplied records when actual equipment is not available.
On 23 September 1999 the Mars Climate Orbiter, a spacecraft that had cost about 327 million dollars and flown for nine months, arrived at Mars and was never heard from again. It had been aiming for an orbit roughly 226 km above the surface. It came in at something closer to 57 km, far inside the atmosphere, and broke apart.
The investigation found the cause within weeks and it was not a broken part. One team had supplied thruster data in pound-force seconds, an imperial unit. The navigation software at NASA expected newton-seconds, the metric unit. Nobody had written the unit down where the other team would see it, and one pound-force second is about 4.45 newton-seconds.
That is an expensive illustration of an ordinary rule. A number without a unit is not a measurement, and a measurement without some statement of its uncertainty invites people to trust it further than it deserves. This lesson is about getting all three parts right.
Build your understanding
Where this fits
The previous lesson built a fair test. This one is about the numbers that come out of it: which units to use, how many digits you are entitled to write down, and how to tell whether two readings actually differ. Every investigation in the rest of this course reports measurements, and the habits set here are the ones that make those reports checkable.
Use three flexible 40-45-minute sessions: units and prefixes; reading an instrument to its limit; precision, accuracy and uncertainty, then the practice set, revision and the exit check. Workshop, review, quiz and exams have separate time.
Part 1 · One system, so that numbers travel
Scientists everywhere use the same set of units, called SI, so that a measurement means the same thing in every laboratory. The base units you will meet most in this course are the metre for length, the kilogram for mass and the second for time, and the useful sizes are made by attaching prefixes. Milli means one thousandth, so 1 mm is 0.001 m and there are 1000 mm in a metre. Centi means one hundredth, so 1 cm is 0.01 m and 1 cm is also 10 mm. Kilo means one thousand, so 1 km is 1000 m and 1 kg is 1000 g. Those three prefixes will cover almost everything you measure for the next three years, and they work the same way on every unit.
Converting is then a matter of asking whether the new unit is bigger or smaller, and whether the number should therefore grow or shrink. A desk 1.2 m long is 120 cm or 1200 mm: the unit got smaller, so the number got larger. A 2500 g bag of flour is 2.5 kg: the unit got larger, so the number got smaller. If you write the conversion down and the number moves the wrong way, you have multiplied where you should have divided, and checking that direction takes two seconds and catches most mistakes. The Mars orbiter failure was this same error at a larger scale, with the unit never written down at all.
Pause and explain
A length is given as 0.75 m. How many millimetres is that, and how do you check the direction?
Check your reasoning after answering
750 mm. A millimetre is a thousandth of a metre, so the unit has become much smaller and the number must become much larger -- multiply by 1000 rather than divide. If the answer had come out as 0.00075, the number shrank while the unit shrank too, which cannot be right for the same physical length.
Part 2 · How many digits you are entitled to
An instrument lets you report everything you can read directly, plus one digit you estimate between the marks. On a ruler marked every millimetre, you can see which millimetre mark the edge falls past, and you can judge roughly whether it is sitting near the middle of the gap, so 64.5 mm is a legitimate reading and 64.53 mm is not: nothing on the ruler supports that third decimal. On a digital balance showing 12.4 g, the last digit shown is already the uncertain one, and writing 12.40 g claims a precision the instrument never offered.
Writing extra digits is not a harmless habit, because every digit you record is a claim about what you know. A student who reports a seedling at 64.53 mm is telling a reader that a difference of a hundredth of a millimetre would have been visible, which invites comparisons the data cannot support. The opposite error is just as real: rounding 64.5 mm to 65 mm throws away a digit you genuinely had. Record what the instrument gives you, no more and no less, and the rest of the analysis stays honest on its own. There is one exception worth knowing now, because it catches people out later. A counted number has no uncertainty at all: if there are 24 seedlings in the tray, there are exactly 24, not 24 ± 1. The rule about estimated digits applies to things you measure on a scale, not to things you count one by one.
Pause and explain
Why is 64.53 mm an improper reading from a millimetre ruler?
Check your reasoning after answering
Because the ruler cannot support the third figure. The marks let you identify the millimetre directly and estimate one digit between them, which gives a tenth of a millimetre at best. A hundredth of a millimetre is beyond anything the scale shows, so recording it asserts knowledge the instrument did not provide.
Part 3 · Precise, accurate, or neither
These two words are used interchangeably in ordinary speech and mean different things here. Precision is how closely repeated measurements agree with each other. Accuracy is how close they are to the true value. You can have either without the other, and the combination that causes the most trouble is precise but inaccurate, because it looks convincing. A ruler with 2 mm worn off the end gives readings of 62, 62 and 63 mm for an object that is really 64 mm: beautifully consistent, and consistently wrong by the same amount.
This is the limit mentioned at the end of the last lesson. Repeating a measurement averages out random scatter -- the small unpredictable differences from reading an angle slightly differently each time -- but it does nothing whatever about a systematic error that pushes every reading in the same direction. A balance that was never zeroed, a stopwatch started late every time, a measuring cylinder read from above rather than at eye level: none of these show up in the spread, and the only defence is to check the instrument against something known before you begin. Checking is usually easy. Put a known 100 g mass on the balance and see whether it reads 100 g; measure a new coin, whose diameter is published, against your ruler; start and stop the stopwatch against a clock for a full 60 seconds. Two minutes of this at the start of an investigation is worth more than an extra hour of readings taken with a faulty instrument.
Pause and explain
Why can repeating a measurement not fix a systematic error?
Check your reasoning after answering
Because the error is the same every time rather than varying. Averaging removes scatter that falls randomly on either side of the true value, but a worn ruler or an unzeroed balance shifts every reading in the same direction by the same amount, so the average is shifted too. Only checking against a known standard reveals it.
Part 4 · Saying how sure you are
The honest way to report a measurement is value, unit and uncertainty, written as something like 64.5 ± 0.5 mm. The last part says you believe the true length lies somewhere between 64.0 and 65.0 mm. Where does the 0.5 come from? For a single reading on a millimetre ruler, half of the smallest division is a reasonable estimate. If you have repeated the measurement, the spread of your readings is better evidence: five readings between 64 and 66 mm support an uncertainty of about 1 mm.
Uncertainty is what lets you answer the question that matters, which is whether two measurements actually differ. If one object measures 64 ± 1 mm and another 65 ± 1 mm, those ranges overlap almost completely, and you have not shown the objects are different sizes at all. If one is 64 ± 1 mm and the other 78 ± 1 mm, the ranges are nowhere near each other and the difference is real. Without the uncertainty, both pairs of numbers look equally like evidence, which is how a great many confident, wrong conclusions get written down. You will see this reasoning used on much larger measurements later in the course. When scientists report that global average temperature has risen by about 1.1 °C since 1900, the figure comes with an uncertainty, and the reason the rise is taken seriously is that it is many times larger than that uncertainty. The habit you are learning on a 65 mm book cover is the same one used there.
Pause and explain
Two readings are 64 ± 1 mm and 65 ± 1 mm. What can you conclude?
Check your reasoning after answering
That no difference has been demonstrated. The first spans 63 to 65 and the second 64 to 66, so the same true length could have produced both readings. The right conclusion is that this instrument cannot separate the two objects, which is not the same as saying they are identical -- a finer instrument might well distinguish them.
Apply the whole unit · Two students, two numbers
Two students measure the width of the same book cover with the same millimetre ruler. One records 65 mm and the other records 64 mm. They disagree about who made a mistake.
Your task
Decide whether either student is wrong, and say what should be recorded.
What your response should show
- Attaches a stated uncertainty to each single reading
- Compares the ranges rather than only the two numbers
- Concludes that the readings are consistent and explains why
- Proposes repeating and a check for systematic error in the instrument
Response guidance — after your attempt
State the uncertainty of a single reading from a millimetre ruler and give both readings with it.
Compare the two ranges and say whether the readings actually conflict.
Describe what the pair should do next and how they should report the result.
A single reading from a millimetre ruler carries an uncertainty of about half a division, so the readings are 65 mm ± 0.5 and 64 mm ± 0.5. The first covers 64.5 to 65.5 and the second 63.5 to 64.5; they meet at 64.5 and so do not actually conflict. Neither student is wrong -- the book's width is simply not something this ruler can pin down more closely than about a millimetre, and 65 mm and 64 mm are both acceptable readings of the same edge. What they should do next is measure several more times, each starting the ruler fresh, and take the mean: if five readings give 64, 65, 64, 65 and 64, the mean is 64.4 mm and the spread supports reporting 64.4 ± 0.5 mm. They should also check the ruler's end for wear, because that would shift both of their readings the same way and the agreement between them would hide it.
Scientific references for this unit
- NASA: Mars Climate Orbiter mishap investigation board report, 1999
- National Institute of Standards and Technology: the International System of Units
- NIST: stating uncertainty in a measurement result
- Royal Society of Chemistry: precision, accuracy and significant figures
Parent / teacher teaching notes
Begin with the literal observation, quantity, or diagram relationship. Ask the learner to explain why the evidence supports the claim, check a relevant unit or boundary, then discuss an alternative. The supplied numbers are illustrative, not real observations. A parent can read aloud and allow a spoken rehearsal before writing. Judge science and evidence rather than political, faith, or family agreement. Use the complete data option whenever an actual procedure is unavailable or unsuitable.
The key idea
Every measurement is a number plus a unit plus an honest statement of how far off it might be. Leaving out any of the three turns a measurement into a claim nobody can check, and in at least one famous case it destroyed a spacecraft.
Worked examples
Example 1 · Measure a starting offset
- Subtract the start from each end.
- Convert centimeters to millimeters.
- Describe variation without claiming perfect exactness.
Example 2 · Transfer to a different case
- Identify the distinction and conditions in this new case.
- Use the relevant mechanism or quantity; preserve units and the chosen boundary.
- Explain why the answer fits and what further evidence would be needed.
Example 3 · Read a scale using a consistent method · a focused example
- State the literal condition or observation in the example.
- Apply the distinction explained in this teaching part.
- Check whether the claim stays within the given evidence.
Avoid these mistakes
- Misleading claim: A measurement without a unit is fully interpretable.
- Misleading claim: Writing more digits automatically improves measurement quality.
- Omitting units, measurement timing, or a model boundary makes the evidence hard to interpret.
- Treating illustrative classroom data as real field measurements or copying a model is not an independent explanation.
Check your understanding
Why is “the leaf is 64” incomplete?
Discuss the answer
The property and unit are absent. A reader cannot know whether 64 describes millimeters, grams, or something else.
How can the offset be avoided?
Discuss the answer
Begin at the zero mark or subtract the recorded start reading from the end reading, using the same units.
Convert 250 mL to liters.
Discuss the answer
250 ÷ 1,000 = 0.25 L; the amount of liquid is unchanged.
Does averaging create unlimited precision?
Discuss the answer
No. A mean summarizes readings, but instrument resolution and method uncertainty still limit what the digits mean.
Sustained scientific explanation and revision
These activities are scheduled in the parent plan. Save a first response, a correction, and the reason for the change in the science notebook.
Activity 1 · Metric Measurement, Units & Uncertainty · Model and explain
Read A Neat Result Is Not Yet an Explanation and the linked unit teaching; use its complete supplied case and diagrams.
Your task
For Metric Measurement, Units & Uncertainty, study Parts 3–4 and attempt the worked case: Find the two lengths in centimeters and millimeters, and state an appropriate measurement limit. Explain the steps before opening the model.
Success criteria
- An accurate scientific claim answering the actual question.
- Relevant evidence or a labeled model with quantities and units when applicable.
- Reasoning that connects evidence with the mechanism rather than only repeats it.
- A specific method, boundary, scale, or alternative-explanation limit.
Hint
Begin with the actual observation, quantity, or diagram relationship. Explain how it bears on the question before making a broad claim.
Response guidance — discuss after your attempt
The lengths are 6.5 cm and 6.4 cm, or 65 mm and 64 mm. The 1 mm difference is observed reading variation; the values do not establish a length exact to many decimal places. Check alignment and use the ruler’s scale consistently.
Activity 2 · Metric Measurement, Units & Uncertainty · Practice, revise and exit
Read A Neat Result Is Not Yet an Explanation and the linked unit teaching; use its complete supplied case and diagrams.
Your task
For Metric Measurement, Units & Uncertainty, complete practice questions 1–6 and both choice checks, then answer independently: A 1.2 kg bag is described as 120 g. Correct the conversion and explain the direction. Save a correction explaining one changed claim or method.
Success criteria
- An accurate scientific claim answering the actual question.
- Relevant evidence or a labeled model with quantities and units when applicable.
- Reasoning that connects evidence with the mechanism rather than only repeats it.
- A specific method, boundary, scale, or alternative-explanation limit.
Hint
Begin with the actual observation, quantity, or diagram relationship. Explain how it bears on the question before making a broad claim.
Response guidance — discuss after your attempt
1.2 kg = 1,200 g. A gram is smaller than a kilogram, so the numerical count grows when the same mass is expressed in grams.
Words to know
- unit
- A defined reference used to express a measurement.
- resolution
- The smallest change a measuring instrument can distinguish under its method.
- evidence
- A relevant observation, measurement, or record used to evaluate a claim.
- system boundary
- The chosen limit specifying what is included and which exchanges cross it.
- uncertainty
- A stated limit in measurement or knowledge, distinct from a careless mistake.
In the real world
Engineers pick materials by density all the time: an airplane needs strong but low-density metals like aluminum so it stays light enough to fly, while the keel of a sailboat is made of dense lead to keep the boat from tipping over.
Exit check
A 1.2 kg bag is described as 120 g. Correct the conversion and explain the direction.
Check your explanation after answering
1.2 kg = 1,200 g. A gram is smaller than a kilogram, so the numerical count grows when the same mass is expressed in grams.
Explain your reasoning without notes. If a key idea is missing, revisit the lesson and try again.
Tables, Graphs & Patterns in Data
Learning goals
- I can build a results table with units in the headings and a column for the mean.
- I can choose between a bar chart and a line graph from the kind of data I have.
- I can tell the difference between reading inside my data and guessing beyond it.
Readings for this unit
Before this unit
Read the chapter opening and the named observation or diagram. Clarify one word or unit that blocks understanding. Recall the relevant earlier measurement, matter, or energy idea.
Study the unit across three 35–45-minute class sessions. Use the worked examples after an attempt and the supplied records when actual equipment is not available.
Hang masses on a spring and measure how far it stretches. With 100 g the spring extends 18 mm; with 200 g, 36 mm; with 300 g, 54 mm. Each extra 100 g adds another 18 mm, which is a pattern you can almost see in the list but not quite.
Plot those three points with mass along the bottom and extension up the side and the pattern is unmistakable: a straight line through the origin. Keep going and at 600 g the extension is 118 mm instead of the 108 mm the line predicts, and at 700 g it is 150 mm. The line has bent, and the graph shows that long before the table does.
That bend is the interesting part of the investigation, and it is the part a badly drawn graph would hide. This lesson is about recording numbers so the pattern can show itself, and about the ways a graph can mislead you or somebody else.
Build your understanding
Where this fits
The last lesson was about taking an honest measurement. This one is about what to do with a column of them. Every investigation in this course ends in a table and usually a graph, and the chapter investigation will ask you to produce both and to say what the shape of your graph means.
Use three flexible 40-45-minute sessions: building the table; choosing and drawing the graph; reading patterns and spotting misleading ones, then the practice set, revision and the exit check. Workshop, review, quiz and exams have separate time.
Part 1 · The table comes first
A results table has one column for the independent variable, one column for each repeat of the dependent variable, and a column for the mean. Units go in the heading, not beside every number, so the heading reads Mass (g) and the cells below it read 100, 200, 300. Writing the unit once in the heading is not only tidier; it forces you to decide what unit you are using before you start recording, which is where the Mars orbiter went wrong in the last lesson. Leave the table ruled and ready before you take a single reading, because inventing columns while measuring is how repeats get lost.
The repeats earn their place. With three readings of the extension at each mass you can take a mean, and you can also see the spread. If the three readings at 300 g are 53, 54 and 55 mm, your mean of 54 mm is solid and your uncertainty is about 1 mm. If they are 48, 54 and 61 mm, the mean is still 54 mm but it now means something much weaker, and reporting it without the spread would hide that completely. A table that shows only means has thrown away half of what was measured, and no graph drawn from it can get that information back. Add one more column if your investigation has anything you had to hold steady but could not control perfectly -- room temperature, say -- and record it each time rather than assuming it stayed put. A column of readings between 19 and 23 degrees Celsius is useful evidence later if your results look odd.
Pause and explain
Why should a results table keep the individual repeats rather than only the mean?
Check your reasoning after answering
Because the mean alone hides how much the readings varied. Three values of 53, 54 and 55 mm and three of 48, 54 and 61 mm give the same average but very different confidence, and only the first supports comparing results that differ by a couple of millimetres. Once the repeats are discarded that information cannot be recovered.
Part 2 · Which graph, and why
The choice follows from the kind of independent variable you have. If it is a measured quantity that could take any value in between, like mass in grams or temperature in degrees Celsius, use a line graph: points plotted and a line or smooth curve drawn through the trend. If it is a set of separate categories, like four different materials or three brands of battery, use a bar chart with gaps between the bars, because there is nothing in between aluminium and copper for a line to pass through. Drawing a line across categories suggests a trend that cannot exist.
Then draw it properly. The independent variable goes along the bottom and the dependent variable up the side, every axis carries a label and a unit, and the scale rises in even steps that use most of the paper. A scale that jumps 0, 10, 50, 100 makes a straight relationship look like a curve, and one that squeezes the data into the bottom corner wastes the graph's whole purpose. Plot the points first with small neat crosses, then decide what line fits; drawing the line you expected and fitting points to it afterwards is a way of seeing what you wanted to see. One more rule that costs nothing: plot every point you measured, including the ones that sit awkwardly off the trend. An outlier left on the graph is information, and a reader can see you noticed it. An outlier quietly deleted turns a record of what happened into a picture of what you hoped would happen, and nobody looking at the finished graph can tell the difference.
Pause and explain
Why is a bar chart right for comparing four different metals but wrong for a range of masses?
Check your reasoning after answering
Because metals are separate categories with nothing between them, while masses form a continuous scale. There is no value halfway between copper and aluminium for a line to represent, so bars with gaps are honest. Masses of 100 and 200 g do have real values in between, so a line correctly shows that the relationship continues through them.
Part 3 · Reading the shape
A straight line through the origin means the two quantities are proportional: double the mass and you double the extension. A straight line that does not pass through the origin means there is a fixed amount there before you start, like a spring that already hangs 5 mm under the weight of its own hook. A curve that flattens out means the effect is reaching a limit, and a curve that steepens means something is accelerating. Naming the shape is the first sentence of your conclusion, and it should come before any attempt to explain why.
Reading between your points is called interpolation and is usually safe: if the line is straight from 100 g to 300 g, a mass of 250 g almost certainly gives about 45 mm. Reading beyond your points is extrapolation and is where investigations go wrong. The spring at the start of this lesson looked perfectly proportional up to 500 g, and anyone extrapolating that line to 700 g would have predicted 126 mm and got 150 mm, because the spring had started to deform permanently. A graph tells you about the range you measured and makes no promises outside it. This is why a published result almost always states the range it covers. A claim that a relationship is proportional means proportional between the smallest and largest values tested, and the sentence that reports it should say so: proportional from 100 to 500 g is a useful finding, while proportional on its own is a claim nobody measured.
Pause and explain
Why is extrapolation riskier than interpolation?
Check your reasoning after answering
Because it assumes the pattern continues where nothing was measured. Between measured points the behaviour is bounded on both sides by data, but beyond the last point a new effect can take over -- a spring beginning to deform, a solution becoming saturated -- and the graph gives no warning. The spring stretched 150 mm at 700 g where the line predicted 126 mm.
Part 4 · Patterns that are not there
Two quantities can rise together without one causing the other. The classic pair is ice cream sales and drowning deaths, both of which peak in July because of hot weather, and the same trap appears in real investigations whenever something you did not record is driving both columns. A graph showing two lines climbing together is evidence that something connects them, and never by itself evidence of which way the connection runs or whether a third thing is behind both.
The other common distortion is the axis that does not start at zero. If a bar chart of average temperatures runs from 20 to 24 degrees Celsius up the side, a difference of 1 degree fills a quarter of the chart and looks dramatic; the same data on an axis from 0 to 25 looks almost flat. Neither picture is a lie about the numbers, and they give completely different impressions, so a reader has to check the axis before trusting the shape. Starting at zero is the safe default, and if you have a reason to do otherwise, say so on the graph. A third trick to watch for is a graph built on very few points. Two points can always be joined by a straight line, so a two-point graph cannot tell you whether the relationship is straight at all; five or six points are what let a curve reveal itself. When you meet a striking graph anywhere, count the points before you believe the shape.
Pause and explain
What should you check before trusting the shape of a graph you did not draw?
Check your reasoning after answering
Where the axes start and what their steps are. A vertical axis beginning at 20 rather than 0 magnifies small differences until they look dramatic, and uneven steps can bend a straight relationship into a curve. The underlying numbers may be perfectly correct while the picture gives an impression they do not support.
Apply the whole unit · Pick the graph and read it
A class measures how long 5 g of sugar takes to dissolve in 100 mL of water at five temperatures: at 20 degrees Celsius it takes 95 s, at 30 it takes 61 s, at 40 it takes 42 s, at 50 it takes 31 s and at 60 it takes 25 s.
Your task
Choose the right graph, describe the pattern, and state what the data does not support.
What your response should show
- Chooses a line graph and justifies it from the type of variable
- Labels both axes with quantity and unit, independent along the bottom
- Describes the shape, including that the effect diminishes, before explaining it
- Distinguishes a safe interpolation from an unsafe extrapolation with reasons
Response guidance — after your attempt
Say which graph type fits this data and why, and state what goes on each axis with its unit.
Describe the shape of the relationship in words, without yet explaining the cause.
Give one value you could read off the graph safely and one you could not, with reasons.
Temperature is a continuous measured quantity, so this is a line graph with temperature in degrees Celsius along the bottom and dissolving time in seconds up the side. The shape is a falling curve that flattens: raising the temperature from 20 to 30 degrees Celsius cuts 34 s off the time, while raising it from 50 to 60 cuts only 6 s. So the time decreases as temperature rises, and each further 10 degrees buys less than the one before. I could safely read off a value at 35 degrees Celsius, since it sits between measured points on a smooth curve and should be around 50 s. I could not read off a value at 90 degrees Celsius: that is well beyond the measured range, the curve is clearly flattening towards some limit, and extrapolating it would also carry me towards 100 degrees Celsius where the water boils and the investigation stops being the same one.
Scientific references for this unit
- Royal Society of Chemistry: presenting experimental data in tables and graphs
- National Oceanic and Atmospheric Administration: reading climate and weather graphs
- Understanding Science, University of California Museum of Paleontology: correlation and causation
- National Institute of Standards and Technology: engineering statistics handbook, graphical methods
Parent / teacher teaching notes
Begin with the literal observation, quantity, or diagram relationship. Ask the learner to explain why the evidence supports the claim, check a relevant unit or boundary, then discuss an alternative. The supplied numbers are illustrative, not real observations. A parent can read aloud and allow a spoken rehearsal before writing. Judge science and evidence rather than political, faith, or family agreement. Use the complete data option whenever an actual procedure is unavailable or unsuitable.
The key idea
A table records what you measured; a graph shows what it does. Choosing the wrong graph, or drawing the axes carelessly, can hide a real pattern or invent one that was never in the numbers.
Worked examples
Example 1 · Compare a cooling record
- Identify the matched start and final time.
- Subtract final from initial temperature for each cup.
- Describe the difference with a method limit.
Example 2 · Transfer to a different case
- Identify the distinction and conditions in this new case.
- Use the relevant mechanism or quantity; preserve units and the chosen boundary.
- Explain why the answer fits and what further evidence would be needed.
Example 3 · Choose a graph for the relationship · a focused example
- State the literal condition or observation in the example.
- Apply the distinction explained in this teaching part.
- Check whether the claim stays within the given evidence.
Avoid these mistakes
- Misleading claim: A missing value is automatically a measured zero.
- Misleading claim: A graph’s association automatically establishes a cause.
- Omitting units, measurement timing, or a model boundary makes the evidence hard to interpret.
- Treating illustrative classroom data as real field measurements or copying a model is not an independent explanation.
Check your understanding
Why must the absent reading stay missing?
Discuss the answer
No measurement establishes its value. Plotting zero would invent an observation and could distort the pattern.
What does the downward pattern describe?
Discuss the answer
Recorded temperature decreases over those times. It does not identify every energy transfer or predict an exact future value by itself.
Does a mean of 7 mean every trial held 7?
Discuss the answer
No. It summarizes three values; the individual loads differ and should remain available.
Can a graph fix a confounded comparison?
Discuss the answer
No. Clear display improves interpretation but cannot remove a problem in how the evidence was collected.
Sustained scientific explanation and revision
These activities are scheduled in the parent plan. Save a first response, a correction, and the reason for the change in the science notebook.
Activity 1 · Tables, Graphs & Patterns in Data · Model and explain
Read A Neat Result Is Not Yet an Explanation and the linked unit teaching; use its complete supplied case and diagrams.
Your task
For Tables, Graphs & Patterns in Data, study Parts 3–4 and attempt the worked case: Find each 10-minute temperature change and explain a supported comparison. Explain the steps before opening the model.
Success criteria
- An accurate scientific claim answering the actual question.
- Relevant evidence or a labeled model with quantities and units when applicable.
- Reasoning that connects evidence with the mechanism rather than only repeats it.
- A specific method, boundary, scale, or alternative-explanation limit.
Hint
Begin with the actual observation, quantity, or diagram relationship. Explain how it bears on the question before making a broad claim.
Response guidance — discuss after your attempt
The unwrapped cup decreases by 9 °C and the wrapped cup by 5 °C. The wrapped cup cools less over ten minutes under the stated comparison. This supports slower cooling if volumes, lids, location, and measurement are matched; it does not prove the wrapping prevents all energy transfer.
Activity 2 · Tables, Graphs & Patterns in Data · Practice, revise and exit
Read A Neat Result Is Not Yet an Explanation and the linked unit teaching; use its complete supplied case and diagrams.
Your task
For Tables, Graphs & Patterns in Data, complete practice questions 1–6 and both choice checks, then answer independently: Two category bars begin at 98 instead of zero and show 100 and 102. What should a reader check? Save a correction explaining one changed claim or method.
Success criteria
- An accurate scientific claim answering the actual question.
- Relevant evidence or a labeled model with quantities and units when applicable.
- Reasoning that connects evidence with the mechanism rather than only repeats it.
- A specific method, boundary, scale, or alternative-explanation limit.
Hint
Begin with the actual observation, quantity, or diagram relationship. Explain how it bears on the question before making a broad claim.
Response guidance — discuss after your attempt
Check the labeled axis and full difference: 2 units. A shortened axis can emphasize that difference, so the graph should make its scale explicit rather than suggest a huge proportional change.
Words to know
- mean
- The sum of comparable values divided by their count.
- pattern
- A relationship observed in records that may guide a test or explanation.
- evidence
- A relevant observation, measurement, or record used to evaluate a claim.
- system boundary
- The chosen limit specifying what is included and which exchanges cross it.
- uncertainty
- A stated limit in measurement or knowledge, distinct from a careless mistake.
In the real world
Meteorologists live in graphs: they plot temperature and pressure as line graphs to track a storm, and news forecasts show you those same trends so you can decide whether to grab an umbrella tomorrow.
Exit check
Two category bars begin at 98 instead of zero and show 100 and 102. What should a reader check?
Check your explanation after answering
Check the labeled axis and full difference: 2 units. A shortened axis can emphasize that difference, so the graph should make its scale explicit rather than suggest a huge proportional change.
Explain your reasoning without notes. If a key idea is missing, revisit the lesson and try again.
Models, Systems & Scientific Explanations
Learning goals
- I can explain why a useful model is always a simplification.
- I can state the limit of a model and give an observation that falls outside it.
- I can explain what makes scientists replace one model with another.
Readings for this unit
Before this unit
Read the chapter opening and the named observation or diagram. Clarify one word or unit that blocks understanding. Recall the relevant earlier measurement, matter, or energy idea.
Study the unit across three 35–45-minute class sessions. Use the worked examples after an attempt and the supplied records when actual equipment is not available.
A classroom globe is a model of Earth, and almost everything about it is wrong. The colours are invented, the mountains are either flat or wildly exaggerated, and the atmosphere is not shown at all. On a globe 30 cm across, the layer of air that holds all the weather would be a skin under 2.4 mm thick, because Earth's radius is about 6371 km and the atmosphere is roughly 100 km deep.
None of that makes the globe a bad model. It is kept because it gets right the thing it is for: the shape of the continents, their relative sizes and positions, and why it is night in one place while it is day in another. A model earns its place by predicting correctly, not by resembling the real thing in every respect.
What matters is knowing which questions a model can answer. Ask the globe about the depth of the atmosphere and it will mislead you badly. Ask it why Australia has summer in December and it is exactly right.
Build your understanding
Where this fits
This lesson closes the first chapter and sets up everything after it. The particle model, circuit diagrams, the water cycle, food webs and the Earth-Moon-Sun system are all models, and each one is introduced in this course with a statement of what it leaves out. Expect that statement every time, and start asking for it when a book does not supply it.
Use three flexible 40-45-minute sessions: what models are for; finding a model's limit; systems, boundaries and how models get replaced, then the practice set, revision and the exit check. Workshop, review, quiz and exams have separate time.
Part 1 · Simplified on purpose
A model keeps the features that matter for one question and throws away the rest. A map of the Underground shows which lines connect at which stations and is deliberately wrong about distance and direction, because a traveller needs the connections and not the geography. A weather model divides the atmosphere into a grid of boxes, perhaps 10 km on a side, and treats everything inside each box as a single set of numbers, even though a real 10 km square of sky contains an enormous amount of structure. That simplification is what makes the calculation possible at all.
The test of a model is not how closely it resembles reality but whether its predictions come true. The particle model treats matter as tiny moving particles with spaces between them, which says nothing about what those particles are actually like, and it still predicts correctly that a gas can be squashed while a liquid can barely be squashed at all, that a smell spreads across a room, and that heating a gas in a sealed container raises its pressure. Those successes are the reason it is taught, and they would still be reasons even if the particles turned out to look nothing like small spheres. It is worth saying plainly that this is not a lowered standard for school science. Professional models work the same way: the weather forecast you check in the morning comes from a grid model that treats a 10 km square of sky as one number, and it is trusted because its predictions are checked against what actually happens, day after day, for years.
Pause and explain
Why is a model judged by its predictions rather than by how closely it resembles the real thing?
Check your reasoning after answering
Because resemblance is not what a model is for. An Underground map is deliberately wrong about distance and still gets travellers to the right platform, and the particle model says nothing about what particles look like while correctly predicting that gases compress and smells spread. A prediction can be checked by anyone; a resemblance cannot be tested.
Part 2 · Where the model stops
Every model has a boundary beyond which it gives the wrong answer, and finding that boundary is not a criticism of the model but part of knowing it. Take the simple particle model and ask it what happens when water freezes. The model says particles in a solid are packed more closely than in a liquid, so a solid should be denser, so ice should sink. Water is about 1.00 g/cm3 and ice is about 0.92 g/cm3, so ice floats, and the simple model has just made a clear, checkable, wrong prediction.
The response is not to abandon the particle model, which works beautifully for almost everything else, but to notice precisely which assumption failed and add what is missing. Water molecules hold each other in an open, six-sided arrangement as they freeze, and that structure props the particles further apart than they sit in the liquid. The amended model keeps everything the old one got right and now gets ice right too. A model's limit, once you find it, is usually an instruction about what detail has to be put back in. That ice floats is not a curiosity either. Because ice forms on the surface rather than sinking, a lake freezes downward from the top and keeps liquid water underneath, which is why fish survive a winter that would otherwise freeze a pond solid. A failed prediction led to a correction, and the correction turned out to explain something that matters a great deal.
Pause and explain
The simple particle model predicts that ice should sink. What does that failure tell you?
Check your reasoning after answering
Exactly which assumption is too simple. The model assumes solids always pack more closely than liquids, and water contradicts it at 0.92 g/cm3 against 1.00. The useful response is to add the missing detail, the open six-sided structure water forms as it freezes, rather than to discard a model that predicts correctly almost everywhere else.
Part 3 · Drawing the boundary of a system
A system is whatever you decide to put inside the line, and drawing that line is a choice you make rather than something nature supplies. Study a sealed terrarium as a system and you can say something exact: light energy crosses the boundary going in, heat crosses it going out, and almost no matter crosses it at all, which is why the same water can cycle inside the glass for years. Draw the boundary around a single plant in that terrarium instead and water and gases now cross it constantly, so the questions you can answer change completely.
Making the boundary explicit is what turns a vague description into something you can check. If you say a system gains 200 J of energy and loses 150 J, you can say where the other 50 J went. If you never said what was inside, the sentence means nothing. This is the move the whole of chapter 5 depends on, where Earth is treated as four interacting systems, and the only reason it works is that the boundary between the atmosphere and the hydrosphere is stated rather than assumed. Boundaries can also be chosen badly. If you investigate how much water a plant loses and draw your system around the pot but not the saucer underneath it, water that drains out and then evaporates has left your system twice and you will count it wrong. Deciding the boundary before you start, and writing it down, prevents almost all of these errors.
Pause and explain
Why does deciding where to draw a system's boundary change which questions you can answer?
Check your reasoning after answering
Because the boundary determines what counts as entering and leaving. Around a sealed terrarium, energy crosses but matter essentially does not, so water cycling can be traced inside. Around one plant in the same terrarium, water and gases cross constantly, so the same investigation now has inputs and outputs it did not have before.
Part 4 · Replacing a model
In 1912 Alfred Wegener proposed that the continents had once been joined and had since moved apart. His evidence was good: the coastlines of South America and Africa fit together, matching fossils of the same land animals appear on both sides of an ocean they could not have crossed, and the same rock layers continue from one continent to the other. Almost every geologist rejected it for forty years, and the reason given was reasonable rather than stubborn -- Wegener could not say what force could possibly push a continent through solid ocean floor.
What changed was not the fossil evidence, which had been there all along, but the discovery in the 1950s and 1960s of the mechanism. Mapping the sea floor revealed ridges where new rock is made, and the magnetic stripes on either side showed it spreading outward at a few centimetres a year. Once there was a process, the old evidence became overwhelming and the model changed within about a decade. Models are replaced when a rival explains everything the old one did and something more, and a mechanism is very often the missing piece. Notice what did not cause the change. Wegener was not won over by argument, and he did not live to see it: he died in Greenland in 1930, more than thirty years before his idea was accepted. The model changed when new measurements of the sea floor arrived, which is the ordinary way these things happen and a slower process than it looks in hindsight.
Pause and explain
Why did Wegener's evidence not convince geologists until the 1960s?
Check your reasoning after answering
Because he had a pattern without a process. Matching coastlines, fossils and rock layers showed that the continents had once been joined but said nothing about how a continent could move through solid ocean floor. Sea-floor spreading supplied that mechanism, and the evidence that had been available for forty years then became persuasive.
Apply the whole unit · Find the limit
A student models the solar system by placing the Sun and eight planets in order along a corridor, with the planets as balls of the right relative sizes, spaced so that all eight fit in 20 m.
Your task
Say what this model gets right, find the thing it gets badly wrong, and explain what that costs.
What your response should show
- Names two questions the model answers correctly
- Identifies distance as the distorted feature and gives a sense of the scale
- States the false conclusion a reader would draw from the distortion
- Keeps the model rather than discarding it, with its limit made explicit
Response guidance — after your attempt
List two questions the model answers correctly.
Identify the feature the model distorts, and say roughly by how much.
Explain what a reader would wrongly conclude if nobody told them about the distortion.
The model gets the order of the planets right, and it gets their relative sizes right, so it answers questions like which planet is fourth from the Sun and whether Jupiter is larger than Earth. What it distorts badly is distance. If the whole system is squeezed into 20 m, the spacing is not to the same scale as the balls, and the gaps between the outer planets are far too small: Neptune is about 30 times further from the Sun than Earth is, and on any honest distance scale the inner four planets would be bunched almost on top of each other while most of the corridor sat empty. A reader shown only this model would conclude that the solar system is fairly evenly filled with planets, when in fact it is overwhelmingly empty space with a few small objects in it. The model is still worth using for order and size, provided the distance distortion is stated.
Scientific references for this unit
- NASA: scale models of the solar system and why distance is hard to show
- US Geological Survey: the development of the theory of plate tectonics
- National Oceanic and Atmospheric Administration: how weather models divide the atmosphere
- Royal Society of Chemistry: the particle model and what it does not explain
Parent / teacher teaching notes
Begin with the literal observation, quantity, or diagram relationship. Ask the learner to explain why the evidence supports the claim, check a relevant unit or boundary, then discuss an alternative. The supplied numbers are illustrative, not real observations. A parent can read aloud and allow a spoken rehearsal before writing. Judge science and evidence rather than political, faith, or family agreement. Use the complete data option whenever an actual procedure is unavailable or unsuitable.
The key idea
A model is a deliberate simplification that is kept because it predicts things correctly, and every one of them is wrong in some way on purpose. Knowing where a model stops working is as much a part of understanding it as knowing what it explains.
Worked examples
Example 1 · A sealed-bottle model
- State that the measured water mass is unchanged.
- Connect cooling with net energy transfer to cooler surroundings.
- Identify what the record cannot quantify.
Example 2 · Transfer to a different case
- Identify the distinction and conditions in this new case.
- Use the relevant mechanism or quantity; preserve units and the chosen boundary.
- Explain why the answer fits and what further evidence would be needed.
Example 3 · Define a system boundary · a focused example
- State the literal condition or observation in the example.
- Apply the distinction explained in this teaching part.
- Check whether the claim stays within the given evidence.
Avoid these mistakes
- Misleading claim: A useful model must reproduce every feature at full size.
- Misleading claim: A sealed system cannot exchange energy.
- Omitting units, measurement timing, or a model boundary makes the evidence hard to interpret.
- Treating illustrative classroom data as real field measurements or copying a model is not an independent explanation.
Check your understanding
Why can a simplified particle drawing be useful?
Discuss the answer
It highlights arrangement and motion at a scale we cannot directly see, provided its symbols and limits are explained.
What distinction does the bottle show?
Discuss the answer
Keeping matter within a boundary is different from preventing energy transfer across it.
What is wrong with extending cooling forever?
Discuss the answer
The surroundings and diminishing temperature difference matter; a trend over a short interval need not continue below room temperature.
Does an omitted population size invalidate every feeding link?
Discuss the answer
No. It limits numerical population predictions, while the represented feeding relationships may still be supported.
Sustained scientific explanation and revision
These activities are scheduled in the parent plan. Save a first response, a correction, and the reason for the change in the science notebook.
Activity 1 · Models, Systems & Scientific Explanations · Model and explain
Read A Neat Result Is Not Yet an Explanation and the linked unit teaching; use its complete supplied case and diagrams.
Your task
For Models, Systems & Scientific Explanations, study Parts 3–4 and attempt the worked case: Explain matter and energy across the chosen boundary, and one limit of the observations. Explain the steps before opening the model.
Success criteria
- An accurate scientific claim answering the actual question.
- Relevant evidence or a labeled model with quantities and units when applicable.
- Reasoning that connects evidence with the mechanism rather than only repeats it.
- A specific method, boundary, scale, or alternative-explanation limit.
Hint
Begin with the actual observation, quantity, or diagram relationship. Explain how it bears on the question before making a broad claim.
Response guidance — discuss after your attempt
The record is consistent with water remaining inside the sealed system while energy transfers to cooler surroundings. The 10 °C drop is not ten joules; calculating energy would require additional information. Constant measured mass does not mean every particle is motionless or that the container blocks energy.
Activity 2 · Models, Systems & Scientific Explanations · Practice, revise and exit
Read A Neat Result Is Not Yet an Explanation and the linked unit teaching; use its complete supplied case and diagrams.
Your task
For Models, Systems & Scientific Explanations, complete practice questions 1–6 and both choice checks, then answer independently: An Earth–Moon model uses a large nearby ball so phases can be seen. Is its distance necessarily correct? Save a correction explaining one changed claim or method.
Success criteria
- An accurate scientific claim answering the actual question.
- Relevant evidence or a labeled model with quantities and units when applicable.
- Reasoning that connects evidence with the mechanism rather than only repeats it.
- A specific method, boundary, scale, or alternative-explanation limit.
Hint
Begin with the actual observation, quantity, or diagram relationship. Explain how it bears on the question before making a broad claim.
Response guidance — discuss after your attempt
No. Its size and distance may be deliberately exaggerated to show illumination. State whether the model is about viewing geometry or numerical scale before using it.
Words to know
- model
- A representation used to explain or predict selected features and relationships.
- system boundary
- The chosen limit separating a system from its surroundings.
- evidence
- A relevant observation, measurement, or record used to evaluate a claim.
- system boundary
- The chosen limit specifying what is included and which exchanges cross it.
- uncertainty
- A stated limit in measurement or knowledge, distinct from a careless mistake.
In the real world
Exit check
An Earth–Moon model uses a large nearby ball so phases can be seen. Is its distance necessarily correct?
Check your explanation after answering
No. Its size and distance may be deliberately exaggerated to show illumination. State whether the model is about viewing geometry or numerical scale before using it.
Explain your reasoning without notes. If a key idea is missing, revisit the lesson and try again.
Problem 1
Answer and explanation
The sunny tray lost 4 g more in the hour, but sunlight and surface area both differ. Greater exposed area could also affect evaporation. Use identical trays with equal water amounts and matched timing, varying only the intended exposure. The present evidence describes a difference without isolating its cause.
Compare the scientific meaning and reasoning after an independent attempt. A different accurate supported response can be correct; human review is required.
Problem 2
Answer and explanation
No. It shows water at that moment; washing, irrigation, or another source could be involved. A cause needs additional information.
Compare the scientific meaning and reasoning after an independent attempt. A different accurate supported response can be correct; human review is required.
Problem 3
Answer and explanation
Width and length both change. Length can affect bending, so the larger load does not isolate width. Keep length, material, support spacing, and counter placement the same, vary width, and use several fresh strips for each treatment. Compare all recorded loads under the same failure rule.
Compare the scientific meaning and reasoning after an independent attempt. A different accurate supported response can be correct; human review is required.
Problem 4
Answer and explanation
No. Counter count is not comparable when counter mass differs. Use identical counters or measure load mass, then repeat the corrected method.
Compare the scientific meaning and reasoning after an independent attempt. A different accurate supported response can be correct; human review is required.
Problem 5
Answer and explanation
The lengths are 6.5 cm and 6.4 cm, or 65 mm and 64 mm. The 1 mm difference is observed reading variation; the values do not establish a length exact to many decimal places. Check alignment and use the ruler’s scale consistently.
Compare the scientific meaning and reasoning after an independent attempt. A different accurate supported response can be correct; human review is required.
Problem 6
Answer and explanation
1.2 kg = 1,200 g. A gram is smaller than a kilogram, so the numerical count grows when the same mass is expressed in grams.
Compare the scientific meaning and reasoning after an independent attempt. A different accurate supported response can be correct; human review is required.
Problem 7
Answer and explanation
The unwrapped cup decreases by 9 °C and the wrapped cup by 5 °C. The wrapped cup cools less over ten minutes under the stated comparison. This supports slower cooling if volumes, lids, location, and measurement are matched; it does not prove the wrapping prevents all energy transfer.
Compare the scientific meaning and reasoning after an independent attempt. A different accurate supported response can be correct; human review is required.
Problem 8
Answer and explanation
Check the labeled axis and full difference: 2 units. A shortened axis can emphasize that difference, so the graph should make its scale explicit rather than suggest a huge proportional change.
Compare the scientific meaning and reasoning after an independent attempt. A different accurate supported response can be correct; human review is required.
Problem 9
Answer and explanation
The record is consistent with water remaining inside the sealed system while energy transfers to cooler surroundings. The 10 °C drop is not ten joules; calculating energy would require additional information. Constant measured mass does not mean every particle is motionless or that the container blocks energy.
Compare the scientific meaning and reasoning after an independent attempt. A different accurate supported response can be correct; human review is required.
Problem 10
Answer and explanation
No. Its size and distance may be deliberately exaggerated to show illumination. State whether the model is about viewing geometry or numerical scale before using it.
Compare the scientific meaning and reasoning after an independent attempt. A different accurate supported response can be correct; human review is required.