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.

Outcome codes: CH-11WS-01 (Year 11) and CH-12WS-01 (Year 12) — Questioning and predicting. Same outcome wording in both years, with a separate code for each. Wording on this page is paraphrased for teaching use — check the official syllabus before treating any line 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 acid dangerous?	How does the concentration of hydrochloric acid affect the rate at which it reacts with magnesium ribbon?
Why are catalysts important?	How does the mass of manganese dioxide added affect the rate at which hydrogen peroxide decomposes?
Is salt interesting?	How does the temperature of water affect the mass of potassium nitrate that dissolves in 100 g of it?

The left column asks for an opinion or a definition. The right column names a variable you can change (concentration, mass of catalyst, temperature) and something you can measure in response (rate, mass dissolved). 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 temperature affects the rate of the reaction between sodium thiosulfate and hydrochloric acid (the "disappearing cross" prac on Rates of Reactions).

Question: How does the temperature of the sodium thiosulfate solution affect the time taken for the cross to disappear when hydrochloric acid is added?
Hypothesis: If the temperature is increased, then the reaction rate will increase (so the cross will disappear sooner), because particles move faster and a greater fraction of collisions have at least the activation energy, so there are more successful collisions per second (collision theory).
Prediction: Across temperatures of 20 °C, 30 °C, 40 °C and 50 °C, the time for the cross to disappear will decrease — roughly halving for each 10 °C rise — and a graph of \(\dfrac{1}{\text{time}}\) (a measure of rate) against temperature will slope upwards.

Notice the prediction is more specific than the hypothesis — it commits to actual temperatures 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 — collision theory, a balanced equation, a periodic trend, a solubility rule, 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 magnesium better than zinc?" needs a defined, measurable criterion for "better" (faster reaction with acid? more gas per gram?) before it's testable.
Unfalsifiable hypotheses — a hypothesis that can't be shown wrong by any possible result isn't doing any work. "Concentration affects reactions somehow" can't fail; "increasing the acid concentration will increase the rate of hydrogen production" can.
Hypothesis that just restates the question — "temperature will affect the rate" 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 reactions often have more than one variable that could plausibly affect the outcome. In a magnesium + acid rate experiment, the reaction is exothermic — so the solution warms up as it goes, and a more concentrated acid warms up more. A careless investigation into concentration is then secretly testing temperature too. 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 (and, here, recording the temperature so you can check it stayed roughly constant). 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 syllabus for this course — 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 chemistry 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 and ideally show the If/then/because hypothesis structure used on this page.

Check yourself¶

Rewrite this into a testable question: "Does the type of metal matter in reactions with acid?"

Answer

Something like: "How does the type of metal affect the rate of hydrogen production when equal-mass, equal-surface-area samples react with 1.0 mol L⁻¹ hydrochloric acid?" — it names what's being changed (the metal) and what's being measured (rate of hydrogen production), and controls the obvious confounding variables (mass, surface area, acid concentration) in the wording itself.
Write a hypothesis (using the If/then/because structure) and a matching prediction for an investigation into how the size of marble chips affects the rate at which they react with hydrochloric acid.

Answer

Hypothesis: If the marble chips are made smaller (for the same total mass), then the reaction rate will increase, because smaller pieces have a larger total surface area, exposing more particles to collisions with acid particles.

Prediction: Large chips, small chips and powdered marble (same mass) will lose mass fastest in the order powder > small > large, and all three will eventually lose the same total mass of \(\ce{CO2}\) (as the acid is in excess).
A student writes the hypothesis: "I think bigger pieces react faster because there's more of them." Explain why this is a weak hypothesis, and rewrite it as a stronger one.

Answer

It's weak because the reasoning is vague ("more of them" — more what?) and, for the same total mass, it's actually wrong — bigger pieces have less surface area. A stronger version: "If the same mass of calcium carbonate is used as smaller pieces, then the rate of reaction with acid will increase, because smaller pieces have a larger surface area, so more particles are exposed and there are more collisions per second." This version is reasoned, falsifiable, and testable.