How to evaluate the statistical significance of experimental results?

Evaluating statistical significance determines whether experimental results reflect a true effect or are likely due to random chance. It primarily involves performing statistical hypothesis testing to quantify the evidence against a null hypothesis. Key principles include establishing a null hypothesis (assuming no effect) and an alternative hypothesis. A significance level (alpha, commonly 0.05) is predefined to denote the probability of rejecting the null hypothesis when it is true (Type I error). Calculating a p-value indicates the probability of observing the obtained results, or more extreme, if the null hypothesis is true; a p-value less than alpha suggests rejecting the null hypothesis, implying the results are statistically significant. Confidence intervals (e.g., 95% CI) provide a range estimate for the effect size. Essential precautions encompass ensuring appropriate sample size, using valid statistical tests, avoiding data dredging or p-hacking, and interpreting significance alongside practical importance and effect size. Actual implementation begins by clearly defining the hypotheses. Select and apply a suitable statistical test (e.g., t-test, ANOVA, chi-square) based on data type and experimental design. Calculate the p-value and compare it to the chosen significance level. If p < alpha, declare statistical significance. Supplement this by reporting effect sizes and confidence intervals. Statistical significance alone doesn't guarantee practical importance; effect size magnitudes and contextual relevance are critical for meaningful interpretation.