ANOVA -Analysis of variance is a form of statistical hypothesis testing used in the analysis of experimental data. A test result is called statistically significant if it is deemed unlikely to have occurred by chance, assuming the truth of the null hypothesis. A statistically significant result, when a probability (p-value) is less than a threshold (significance level), justifies the rejection of the null hypothesis, but only if the a priori probability of the null hypothesis is not high. Typically, the null hypothesis is that all groups are simply random samples of the same population. For example, when studying the effect of different treatments on similar samples of patients, the null hypothesis would be that all treatments have the same effect. Rejecting the null hypothesis would imply that different treatments result in altered effects. By construction, hypothesis testing limits the rate of Type I errors (false positives) to a significance level. Experimenters also wish to limit Type II errors (false negatives). The rate of Type II errors depends largely on sample size, significance level (when the standard of proof is high, the chances of overlooking a discovery are also high) and effect size.
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