Hypothesis testing is a statistical method used to evaluate whether a hypothesis about a population parameter is supported by the available evidence from a sample of data.
The process involves formulating a null hypothesis, which is a statement of no effect or no difference between two groups, and an alternative hypothesis, which is a statement that there is a significant effect or difference. Then, a significance level (alpha) is chosen to determine the threshold for rejecting the null hypothesis.
Next, a test statistic is calculated from the sample data, which measures how far the observed data deviates from what would be expected under the null hypothesis. This test statistic is compared to a critical value determined from the chosen significance level and degrees of freedom.
If the test statistic is greater than the critical value, then the null hypothesis is rejected in favor of the alternative hypothesis, and it is concluded that there is evidence to support the hypothesis. If the test statistic is less than the critical value, then the null hypothesis is not rejected, and it is concluded that there is not enough evidence to support the hypothesis.
It is important to note that hypothesis testing cannot prove a hypothesis to be true, but rather it can only reject or fail to reject the null hypothesis. Additionally, the results of hypothesis testing depend on the assumptions made about the data and the statistical test used, and therefore, it is important to carefully consider the appropriateness of these assumptions before interpreting the results.
