AeroLab Reading room

Biology / A2 level

Practical planning, analysis and evaluation

Planning begins with a testable question, operational definitions and a method that can produce valid comparisons.

Learning objectives

The big idea

Planning begins with a testable question, operational definitions and a method that can produce valid comparisons. Select independent and dependent variables, suitable controls, independent replication and an appropriate range; ethical and safety constraints shape what can be investigated.

Graphing and statistical analysis should match the data and study design. Correlation does not establish causation, and statistical significance does not automatically imply a large biological effect. Improvements should target identified limitations, such as biased sampling or imprecise timing, rather than simply requesting more expensive apparatus.

Worked example

A treatment mean is 24 units and a control mean is 20. Find the percentage difference relative to the control.

Follow the solution

The difference is 4 units; 4/20×100=20%. Variability and design are still needed to evaluate the effect.

Investigate and explain

Using the worked example as supplied data, predict the effect of changing one condition. Draw a labelled model, record your reasoning and identify a variable that would need controlling. No human, animal or microbial experiment is required.

No exact PhET model is claimed for this topic. Use diagrams and supplied-data reasoning instead.

Open the interactive investigation

Check your understanding

1. Which improvement directly addresses sampling bias across a habitat?

  1. Use a justified random or stratified sampling design
  2. Round every result to more decimals
  3. Repeat only the easiest site
Check the answer and reasoning

Use a justified random or stratified sampling design

Sampling design targets representativeness; numerical precision cannot fix selection bias.

2. A treatment mean is 24 units and a control mean is 20. Find the percentage difference relative to the control.

  1. 4%
  2. 20%
  3. 120%
Check the answer and reasoning

20%

The difference is 4 units; 4/20×100=20%. Variability and design are still needed to evaluate the effect.

3. Does a statistically significant association by itself prove a causal mechanism?

  1. Yes
  2. Only with a small p-value
  3. No
Check the answer and reasoning

No

Confounding and design limitations can remain despite a low p-value; causation needs additional evidence.