Alpha (ɑ) represents the significance level in hypothesis testing, typically set at 0.05 or 0.01, which is the probability of making a Type I error. This is the error of rejecting the null hypothesis when it is actually true. In the context of chi-square tests for homogeneity or independence, alpha plays a crucial role in determining whether to reject the null hypothesis based on the calculated p-value and helps researchers make informed decisions regarding statistical relationships between categorical variables.