Statistical Power
The probability that a test will detect an effect of a given size if it truly exists; higher power means a lower risk of a false negative.
Statistical power is the probability that a hypothesis test will correctly reject a false null hypothesis, in other words, detect a real effect. Power depends on the size of the effect, the sample size, the significance level, and the variability of the data. A common target is 0.80, meaning an 80 percent chance of detecting the assumed effect. Underpowered studies frequently miss real effects and produce unstable estimates.
An a priori power analysis works backwards: given an expected effect size, an alpha level, and a desired power, it tells you the sample size you need before data collection begins.
For a thesis, a power analysis is the standard way to justify your sample size in the methodology chapter, and it helps you interpret null findings honestly in the limitations section, since a nonsignificant result from a small sample may reflect low power rather than no effect.
Writing the thesis this term belongs to?
CiteDash takes a thesis from research question to a compiled document, with AI that cites only real papers and verifies every claim against its source.