Scientific questions and observations

A larger sample reduces the effect of chance individual differences. Link sample size with reliability.

Failure to germinate is an observation but defective seeds have not been proven as the cause. Identify unverified causes.

The direct answer is A: variation may indicate error or instability. An average combines measurements into one summary value, but it hides how far the individual readings are from one another. If three readings differ greatly, the scientist should check instrument calibration, reading technique, changing conditions, recording mistakes, or whether the phenomenon itself is variable. Option A is correct because large spread can signal error, poor control, or an unstable process, so the cause must be investigated before trusting the average. Option B is wrong because averages can be useful when measurements are reasonably consistent and are interpreted with spread. Option C is wrong because differing values do not prove that every measurement is wrong; some variation may be genuine. Option D is wrong because the largest value has no automatic claim to be correct. A careful report gives the individual readings and, where suitable, an average together with a measure or description of spread. Memory cue: never let an average hide unusual variation—inspect the data first.

Only temperature is changed while other major conditions remain constant. Look for one independent variable in a fair comparison.

Daily change in height provides quantitative evidence of growth rate. Convert vague descriptions into measurements.

Prior knowledge may influence evaluation. Understand the importance of objective criteria or blind assessment.

This record includes initial value final value unit and time. Identify the components of complete data.

Similar results on different days increase confidence in the conclusion. Link reproducibility with repetition.

An incorrect starting point may bias all height measurements. Understand the importance of a standard reference point.

The first conclusion is limited to the test performed. Choose conclusions that remain within the limits of evidence.

The term best must be defined using a measurable criterion and equal conditions. Identify operational definitions.

Standardised categories make descriptions consistent. Choose clear criteria for objective recording.

Rainfall and moisture changing together show association but other causes may exist. Distinguish correlation from causation.

A complete procedural record is necessary for transparency and reproduction. Understand the importance of recording every change.

An incorrect zero may make all readings consistently high or low. Link zero error with systematic error.

Numerical values are more useful for precise comparison. Choose quantitative data in comparison-based investigations.

Selecting already tall plants makes the sample unrepresentative. Identify random or unbiased sampling.

Measurements at two time points allow change per unit time to be calculated. Look for both quantity and time when finding a rate.

The control group provides a baseline and helps isolate the actual effect of treatment. Understand the role of controls.

The rise in level is a measured change without a causal claim. Separate direct data from interpretation.

Too many outcomes may make a question broad and analysis complex. Choose one clear primary outcome.

Area is a two-dimensional measurement so one dimension may be insufficient. Identify the required dimensions of a quantity.

A testable question should match available methods and instruments. Treat feasibility as a feature of a scientific question.

Scientific investigation proceeds systematically from a question to an evidence-based conclusion. Remember the correct sequence of steps.

Initial size is a confounding variable and actual growth change must be compared. Control initial differences in exams.