Hypothesis and experimentation

Changing both fertilizer and light makes the true cause of growth unclear. In exams identify confounding variables.

Thirty seconds is a possible anomaly that should be checked before exclusion. In exams investigate anomalous data.

The improved statement clearly identifies both the independent variable and measured outcome. In exams make hypotheses measurable.

Closely grouped readings show reliability but distance from the true value shows poor accuracy. In exams distinguish accuracy from reliability.

Highly unequal sample sizes can make comparison biased and less reliable. In exams check similarity of group sizes.

Moving the lamp closer changes the light intensity reaching the plant. In exams connect the changed distance with the actual causal variable.

With only one difference the cause of the result can be identified more clearly. In exams look for a fair cause-and-effect test.

Plant water status or posture may vary at different times causing inconsistent measurements. In exams treat timing as a controlled condition.

Drawing a very broad conclusion from limited data is overgeneralisation. In exams limit conclusions to the available evidence.

An error of the same direction in every reading indicates systematic error. In exams consider zero error and faulty calibration.

Selecting only favourable outcomes creates selection bias and weakens the conclusion. In exams include all valid data.

Different cars may influence the result so only surface type should change. In exams change one factor at a time.

Transpiration can be measured through water loss over a fixed time. In exams express the outcome of a hypothesis measurably.

Greater spread shows poorer consistency and lower reliability. In exams examine variation as well as the mean.

Considering repetition and variation along with the mean strengthens reliability. In exams examine both the amount and consistency of evidence.

A scientific hypothesis must be capable of being shown wrong by evidence. In exams link falsifiability with testability.

Changing enzyme amount prevents isolation of the temperature effect. In exams control other influential factors.

A positive control gives a known response showing that the method and apparatus work. In exams identify it as the group expected to show an effect.

Growth is determined from the difference between final and initial measurements. In exams record a baseline when measuring change.

An instrument with finer divisions is needed to detect small changes. In exams choose resolution according to the expected change.

Different rooms may have different echo and absorption which confound the effect of distance. In exams control environmental variables.

An inconsistent procedure or uncontrolled variables can cause large variation. In exams examine the method when replication fails.

An individual plant characteristic may affect the result. In exams look for adequate sample size in biological experiments.

An extreme anomalous value can pull the mean away from the typical trend. In exams inspect data spread along with the mean.

In a blind test the observer or participant does not know the treatment identity reducing expectation bias. In exams identify objective observation.