Sample Size Calculation in Clinical Research
What you need before calculating sample size, the common formulas, and how to justify your assumptions.
Biostatistics
Understanding p-values and Confidence Intervals
What p-values and confidence intervals actually mean — and the common misinterpretations to avoid in your thesis.
Biostatistics
p-values and confidence intervals appear in almost every results section, yet they are among the most misunderstood concepts in medical research.
What a p-value is
The p-value is the probability of observing data at least as extreme as yours if the null hypothesis were true. It is not the probability that the null hypothesis is true, and it does not measure the size or importance of an effect.
What a confidence interval adds
A 95% confidence interval gives a range of values compatible with your data for the effect you estimated — a difference in means, an odds ratio, a risk ratio. It shows both the direction and the precision of the estimate.
Common mistakes
- Treating p = 0.049 and p = 0.051 as fundamentally different results.
- Saying “no difference” when p > 0.05 — the study may simply be underpowered.
- Reporting only p-values without effect sizes and confidence intervals.
- Equating statistical significance with clinical importance.
How to report
Report the effect estimate, its 95% CI and the exact p-value, e.g. “OR 2.1 (95% CI 1.3–3.4), p = 0.003”. Then interpret the clinical meaning of the estimate, not only whether it crossed 0.05.
This article is for general educational purposes. Always follow your institution’s guidelines and your guide’s advice.