All Activities
Science

Statistical Thinking in Science

Overview

Students explore how scientists use statistics to determine whether results are real or due to chance, developing the quantitative literacy needed to evaluate scientific evidence critically.

Learning Objective
Students understand how statistical analysis is used to evaluate scientific claims and can apply basic statistical concepts to interpret experimental data.

Resources needed

  • None — or data from a previous practical investigation

Lesson stages

0 / 7 done
  1. 1 Ask: if a new drug seems to work in 10 patients, how confident can we be? What if it works in 10,000 patients?
  2. 2 Introduce sample size: larger samples give more reliable conclusions. Why? Random variation averages out over large samples.
  3. 3 Introduce the mean and standard deviation: the mean describes the central tendency; the standard deviation describes how spread out the data is.
  4. 4 Introduce statistical significance: a result is statistically significant if it is unlikely to have occurred by chance. Expressed as a p-value.
  5. 5 Explain p-value: p less than 0.05 means there is less than a 5% probability the result is due to chance. This is the conventional threshold for significance.
  6. 6 Introduce the difference between correlation and causation: two variables that increase together are correlated, but this does not mean one causes the other.
  7. 7 Apply: shoe size and reading ability are correlated in children — both increase with age. Shoe size does not cause reading ability.

Tap a step to mark it as done.

Variations

  • Analyse a real dataset from a class experiment — calculate mean, standard deviation, and comment on variability.
  • Evaluate a reported scientific finding — was the sample large enough? Was the result statistically significant?
  • Discuss publication bias: studies with positive results are more likely to be published, potentially distorting our view of the evidence.
More information

Teach: mean, standard deviation, sample size, p-value, significance, correlation, causation, bias. The correlation-causation distinction is the most important statistical thinking concept for everyday scientific literacy.

Focus on sample size, mean, and the concept of statistical significance before introducing p-values and correlation.

Can students explain why larger sample sizes give more reliable results? Can they distinguish between correlation and causation and give an example where the two are confused?

Use data from any previous class investigation. Calculations require only arithmetic. No statistical software needed.

Students often interpret any correlation as causation. Provide multiple examples — ice cream sales and drowning rates both peak in summer, but ice cream does not cause drowning. The habit of asking 'could there be a confounding variable?' is the key critical thinking skill.

Statistical literacy is essential for evaluating health claims, scientific news, and policy decisions. It is one of the most transferable skills in science education and applies in every field from medicine to economics to sports.

Activity details
Secondary Challenging 25 minutes no prep Whole Class, Small Group Indoor & Outdoor
Topics
Statistics Scientific Evidence Data Analysis