Questioning and Predicting

Syllabus mapping

Working Scientifically outcome — Questioning and predicting. Covers identifying a problem, formulating a testable question and a hypothesis, and using scientific knowledge to make a reasoned prediction. This isn't tied to a single focus-area dot point — it's the outcome assessed whenever a prac, depth study, or extended-response question asks you to frame an investigation from scratch rather than follow one already written for you.

Exact NESA outcome code TODO — confirm against the official syllabus PDF (Resources) before treating any wording on this page as verbatim NESA text.

You need to know: the difference between a question, a hypothesis, and a prediction — and how to turn a vague "I wonder if..." into something you can actually put a number on in a lab.

Core

What makes a question testable

A scientific question has to be answerable by observation or experiment — not by opinion, and not by looking up a definition. It needs at least one thing you can change or measure (a variable), and it needs to be specific enough that two different people testing it would know whether they'd got the same answer.

Not testable	Testable
Is electricity dangerous?	How does the current through a wire affect how much it heats up?
Why is gravity important?	How does drop height affect the time taken for an object to fall?
Are pendulums interesting?	How does the length of a pendulum affect its period?

The left column asks for an opinion or a definition. The right column names a variable you can change (drop height, pendulum length, current) and something you can measure in response (time, temperature, period). That pairing — something you change, something you measure — is the core of every testable question in this course, and it's exactly the independent/dependent variable pairing you'll use again in Planning.

A useful default template: "How does [thing you change] affect [thing you measure]?" It won't fit every investigation, but it's a reliable way to check whether a vague idea is actually testable yet.

Hypothesis vs prediction

These two get used almost interchangeably in casual speech, but they mean different things in a science report and examiners will distinguish between them.

• A hypothesis is a tentative, testable explanation for a relationship between variables — a statement of what you think is going on, and often why. It should be falsifiable: there has to be a result that would prove it wrong.
• A prediction is the specific, expected outcome of this particular test, derived from the hypothesis (and often from prior knowledge or a model). It's what you'd expect to see on the page in front of you, not the general claim behind it.

A common, reliable structure for a hypothesis: "If [independent variable] is changed in this way, then [dependent variable] will change in this way, because [reasoned mechanism]." The "because" clause is what turns a guess into a hypothesis — it shows you're predicting from a model of how the system works, not just picking an answer.

Worked example

Scenario: investigating how the angle of an inclined plane affects a trolley's acceleration down the ramp (the prac referenced in Motion on Inclined Planes).

• Question: How does the angle of an inclined plane affect the acceleration of a trolley released from rest on it?
• Hypothesis: If the angle of the incline is increased, then the trolley's acceleration will increase, because a steeper incline increases the component of gravitational force acting parallel to the slope (\(g\sin\theta\)), while the perpendicular component — and therefore friction — decreases.
• Prediction: Across angles of 10°, 20°, 30°, 40° and 50°, the measured acceleration will increase with angle, and a graph of acceleration against \(\sin\theta\) will be approximately linear (see Motion on Inclined Planes — Core).

Notice the prediction is more specific than the hypothesis — it commits to actual angles and to what a graph of the result should look like, which is what makes it possible to judge afterwards whether the prediction was actually right.

Predicting from a model, not guessing

A prediction should be reasoned from something you already know — a formula, a trend from a related experiment, background research, or a pattern from earlier data — not picked at random. If you can't explain why you expect a particular outcome, you have a guess, not a prediction. This is also why the "because" clause in a hypothesis matters for marks: "Explain" and "Justify" command-verb questions (see the command verb glossary) are explicitly asking you to show that reasoning, not just state the expected result.

Common pitfalls

• Non-testable or opinion-based questions — "Is X better than Y?" needs a defined, measurable criterion for "better" before it's testable.
• Unfalsifiable hypotheses — a hypothesis that can't be shown wrong by any possible result isn't doing any work. "Forces affect motion somehow" can't fail; "increasing the pulling force will increase the trolley's acceleration, in proportion to the force" can.
• Hypothesis that just restates the question — "the length will affect the period" restates the question without the reasoned relationship a good hypothesis needs.
• Confusing a prediction with the aim — the aim is what you're investigating in general; the prediction is the specific numeric or graphical outcome you expect this time.

Advanced

Multi-variable systems and confounding. Real systems often have more than one variable that could plausibly affect the outcome — a pendulum's period depends on length, but a careless investigation might also let the release angle or the mass vary between trials. A strong hypothesis names the one relationship being tested and implicitly assumes everything else is held constant — which is exactly the job Planning does explicitly, by listing controlled variables. If your hypothesis doesn't obviously map onto "one independent variable, one dependent variable, everything else fixed," that's usually a sign the question needs to be narrowed before you can test it cleanly.

Extension

Beyond the Physics 11–12 syllabus — won't appear in the HSC, included for interest / depth study inspiration.

📎 Depth study idea

University-level and professional science almost never talks about "hypotheses" in isolation — it talks about the null hypothesis (\(H_0\)): the assumption that there is no relationship between the variables, which is the thing you're actually trying to statistically rule out. Under this framework, you don't "prove" your hypothesis correct; you gather enough evidence that the null hypothesis becomes implausible, and reject it in favour of your alternative hypothesis. This reframing — testing to disprove a "no effect" baseline, rather than testing to confirm your own idea — is the foundation of statistical hypothesis testing (p-values, significance levels), and is a genuinely good depth study angle if you want to connect a physics investigation to how professional experimental science actually reports its confidence in a result.

Video/visual resources

• 🎥 Khan Academy — TODO: source a hypothesis vs prediction / scientific method explainer. Must distinguish the two terms clearly (not just define "scientific method" generically) and ideally show the If/then/because hypothesis structure used on this page. Essential.
• 🎥 Physics High — TODO: check, but a dedicated video on this specific WS skill is unlikely from a physics-content channel — more realistic to skip this line for this page, or substitute a general NSW Science (not physics-specific) source if one exists.

Check yourself

1. Rewrite this into a testable question: "Does the material of a wire matter for electricity?"

   Answer

   Something like: "How does the material a wire is made from affect its resistance, for wires of the same length and cross-sectional area?" — it names what's being changed (material) and what's being measured (resistance), and controls the obvious confounding variables (length, thickness) in the wording itself.

2. Write a hypothesis (using the If/then/because structure) and a matching prediction for an investigation into how the mass on the end of a spring affects the spring's extension.

   Answer

   Hypothesis: If the mass hung on the spring is increased, then the extension will increase, because a larger mass exerts a larger gravitational force on the spring, and extension is proportional to applied force (Hooke's Law).

   Prediction: Across a range of masses (e.g. 50 g to 500 g in 50 g steps), extension will increase roughly linearly with mass, and a graph of extension against mass will pass close to the origin.

3. A student writes the hypothesis: "I think heavier objects fall faster." Explain why this is a weak hypothesis, and rewrite it as a stronger one.

   Answer

   It's weak because it isn't reasoned (no "because" clause) and, in the absence of air resistance, it's actually false — it doesn't reflect an understanding of the physics. A stronger version: "If two objects of different mass are dropped from the same height in the absence of significant air resistance, then they will reach the ground at the same time, because gravitational acceleration is independent of mass." This version is falsifiable, reasoned, and testable by timing drops of objects with different masses but similar shape/air resistance.