Statistics Foundation · Lesson 4.5
Sample size and study design.
Sample size planning connects mathematics with responsible study design. This lesson explains how sample size, variability, effect size, power, allocation, precision, dropout and bias shape the quality of statistical evidence.
Lesson route
Move from inference interpretation to study planning.
Previous lessons interpreted confidence intervals, p-values and power. This lesson moves earlier in the research process: how should a study be designed so that the evidence is precise, powerful and credible?
0–15 min
Why sample size matters
Understand sample size as a design decision that controls precision, uncertainty and ability to detect meaningful effects.
15–40 min
Precision planning
Learn how margin of error, variability and confidence level determine the sample size needed for a desired interval width.
40–70 min
Power planning
Study how sample size affects the probability of detecting a meaningful effect when that effect truly exists.
70–100 min
Effect size and variability
Explore how smaller effects and higher variability require larger samples.
100–125 min
Allocation and design
Understand how balanced and unbalanced group sizes affect precision in comparative studies.
125–155 min
Quality of evidence
Connect sample size to study design, bias, representativeness, feasibility, ethics and reporting.
Mastery checklist
Students should connect sample size to evidence quality.
Calculate sample size for a desired margin of error.
Explain why n grows with variability.
Explain why smaller effects require larger samples.
Adjust sample size for dropout.
Compare balanced and unbalanced allocation.
Explain diminishing returns from increasing n.
Distinguish random error from systematic bias.
Report sample size assumptions transparently.