AC9S6I01: Pose investigable questions to identify patterns and test relationships and make reasoned predictions

Learning intention: We are learning to pose questions that evidence can answer and to predict outcomes using scientific reasons.

Curriculum focus: Pose investigable questions to identify patterns and test relationships, and make reasoned predictions.

Success criteria

• identify variables that can be changed, measured or observed
• match a question to available materials, methods and data
• write an if–then–because prediction supported by prior evidence

investigable question

a question answerable through safe, feasible observation or measurement

variable

a factor that can change or be measured

changed variable

the factor deliberately varied to test a relationship

measured variable

the outcome observed or measured in response

controlled variable

an influential condition deliberately kept the same

pattern

a repeated or directional feature in observations or data

relationship

a way in which two measured variables vary together

prediction

a justified statement of the expected outcome made before testing

confounding variable

an extra changing factor that prevents a clear interpretation

operational measure

a precise observable way to represent an idea such as strength or warmth

Reliable routine

1. Identify the phenomenon and the evidence that could answer a question.
2. Name one variable to change or compare and one outcome to measure or observe.
3. Check that available materials, time and instruments can produce the required evidence.
4. Remove unsafe, unethical, subjective or unrealistic elements and define vague terms.
5. Use prior observations or scientific knowledge to write an if–then–because prediction.
6. After collecting evidence, describe patterns carefully and acknowledge limits.

What makes a question investigable?

An investigable question can be answered with safe observations or measurements using available time, equipment and materials. A useful frame is: How does [variable changed] affect [variable measured] when [important controls] stay the same?

Patterns and relationships

A pattern describes how observations repeat or change across time, place or categories. A relationship asks how two measured variables vary together. A relationship in observational data is an association; it does not by itself prove that one variable caused the other.

Match question, method and data

Every named comparison must be tested and every claimed outcome must be measured. A method comparing pure and soapy water cannot answer a question about an untested liquid. A table of germination counts cannot answer a question about unrecorded seedling height.

Make vague words measurable

Replace words such as better, stronger, warmer or healthier with an operational measure: maximum load, force, temperature change or growth over a stated time. Include quantities and units when they make the test reproducible.

Reasoned predictions

Use: If [change], then [expected direction], because [relevant evidence or scientific mechanism]. The reason must explain the expected link rather than repeat the “then” clause. Predictions are written before results and may be revised when evidence disagrees.

Fair comparisons and confounding

Changing one target factor while keeping other influential conditions constant helps isolate a relationship. If two factors change together, the outcome is confounded: evidence cannot show which factor mattered.

Safety, ethics and feasibility

A measurable question may still be unsuitable if it risks harm, disturbs wildlife, requires an unrealistic timescale or exceeds available instruments. Redesign the question or use safe existing data rather than forcing a test.

Limits and careful claims

Instrument resolution, narrow ranges, small samples and missing comparison groups restrict what evidence can show. Predict cautiously beyond tested conditions and never turn an association into a causal claim without a design that isolates cause.

Important questions and answers

Q: Must an investigable question avoid yes/no wording? A: No; evidence and measurement matter more than grammar. Q: Must a prediction be correct? A: No; it must be justified before testing. Q: Can every scientific question be tested in class? A: No; safety, ethics, scale and equipment impose limits.

Assessment and mastery

Name the changed and measured variables, check that materials and data match the question, identify unavailable comparisons or confounders precisely, and justify predictions with an evidence-based mechanism. State what the evidence cannot establish.

• Every question can be tested. Questions may be subjective, unsafe, unethical, too broad or impossible with available time and equipment.
• A prediction is a random guess. A scientific prediction uses observations, prior data or accepted scientific knowledge to justify an expected outcome.
• Changing more variables makes a stronger test. Changing multiple influential factors confounds the result and weakens the conclusion about each relationship.
• A yes/no question is automatically invalid. Yes/no wording can be investigable when clear evidence can decide between the alternatives.
• A pattern proves cause. An observed relationship may be an association caused by another factor; causal claims require an appropriate controlled design.

• What is the quickest test for an investigable question? Ask what will change or be compared, what will be measured, and whether the required evidence can be collected safely with available methods.
• Does a prediction need an exact number? No. A justified direction is often stronger than false precision, especially when evidence is limited.
• What if the prediction is wrong? Report the result honestly and use it to revise the explanation or plan another investigation.
• Can paired data prove cause? Not usually. Paired observations can reveal an association; a fair comparison is needed to isolate a possible cause.

• Use the frame “How does ___ affect ___ when ___ stays the same?”
• Name the missing measurement or unavailable comparison precisely.
• Match each question to the actual equipment, procedure and recorded data.
• In predictions, make the because clause explain the expected relationship.
• Distinguish an observed association from a demonstrated causal effect.
• State relevant safety, ethical, scale, sampling or instrument limitations.

• Support: work with one short example, highlighted evidence and a structured response frame.
• Core: complete an unseen example independently and justify the decisive evidence.
• Extend: compare plausible alternatives, explain limitations and create a new example within the Year 6 boundary.

Write a safe investigable question with named variables, add a justified prediction, and identify one limit on the evidence.

Evidence of mastery: The student poses safe, feasible questions aligned to available methods and data; identifies patterns and relationships without overstating cause; and makes directional predictions supported by relevant evidence or scientific mechanisms.