<\/span><\/h2>\nWhen conducting a hypothesis test, researchers set up two competing hypotheses: the null hypothesis (H0) and the alternative hypothesis (H1). The null hypothesis assumes that there is no significant difference or relationship between variables, while the alternative hypothesis asserts that there is a significant difference or relationship.<\/p>\n
Statistical hypothesis testing involves calculating a p-value, which reflects the probability of obtaining the observed results under the assumption that the null hypothesis is true. The p-value indicates the strength of evidence against H0. In hypothesis testing, we compare this p-value with the predetermined \u03b1 value to make a decision.<\/p>\n
What does \u03b1 value mean?<\/strong>
\nThe \u03b1 value, commonly set at 0.05 or 0.01, represents the threshold or level of significance. It defines the maximum probability of observing an effect as extreme as the one observed, assuming the null hypothesis is true, beyond which we reject the null hypothesis.<\/p>\nSetting the \u03b1 value is crucial as it determines the balance between Type I and Type II errors. Type I error occurs when we reject the null hypothesis while it is actually true, and Type II error happens when we fail to reject the null hypothesis while it is false. By defining the \u03b1 value, we control the risk of making a Type I error.<\/p>\n
For instance, if we set the \u03b1 value at 0.05, we are accepting a 5% risk of rejecting the null hypothesis when it is true. Similarly, an \u03b1 value of 0.01 limits the risk to 1%.<\/p>\n