Null Hypothesis / H0

CORE DEFINITION

In inferential statistics, the null hypothesis (also translated as 'virtual hypothesis' or 'original hypothesis', symbol: H0) is a type of hypothesis used in statistical testing. The null hypothesis generally states that there is no effect or no difference, and it is the hypothesis that researchers typically aim to disprove. The alternative hypothesis is the opposite of the null hypothesis, representing the claim that researchers hope to support by rejecting the null hypothesis. Mathematically, the null and alternative hypotheses are equivalent in status, but in practical applications, one hypothesis is often emphasized as the one that should or is expected to be true. For example, in correlation tests, the null hypothesis is usually 'there is no association between the two variables', while in independence tests, the null hypothesis is usually 'the two variables are not independent'. If a statistical test rejects the null hypothesis (the conclusion does not support the null hypothesis) when the null hypothesis is actually true, this is called a Type I error. Conversely, if the test fails to reject the null hypothesis when the alternative hypothesis is actually true, this is called a Type II error.