Understanding Hypothesis Testing 🐸
Hypothesis testing is a crucial component of statistical inference, allowing us to make inferences about populations based on sample data. At its core, hypothesis testing involves formulating two competing hypotheses: the null hypothesis (H0) and the alternative hypothesis (H1).
The Null Hypothesis
The null hypothesis is a statement that there is no effect or no difference. Its main purpose is to challenge or disprove it in favor of the alternative hypothesis.
P-Values Explained 📊
The p-value helps determine the significance of your results. A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis. Learn more about p-values here.
The Power of Statistical Tests
Power refers to the probability that a statistical test will detect a true effect. It is influenced by the sample size, effect size, and significance level. Discover more on power analysis in statistics.