Type I And Type II Errors
A Type I error is rejecting a true null hypothesis (false positive); a Type II error is failing to reject a false one (false negative).
Hypothesis testing can go wrong in two ways. A Type I error occurs when you reject a null hypothesis that is actually true, declaring an effect that does not exist; its probability is capped by the significance level, alpha. A Type II error occurs when you fail to reject a null hypothesis that is actually false, missing a real effect; its probability is called beta, and one minus beta is statistical power.
The two risks trade off: lowering alpha to avoid false positives makes false negatives more likely unless you increase the sample size or reduce noise. Running many tests without correction inflates the chance of at least one Type I error.
In a thesis, this framework justifies your alpha level, your power analysis, and any multiple-comparison corrections, and it disciplines interpretation: a nonsignificant result may be a Type II error, not evidence that no effect exists.
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