# Hypothesis testing

Using the degree of freedom value as 24 and a 5% level of significance, a look at the t-value distribution table gives a value of 2.064. Comparing this value against the computed value of 2.247 indicates that the calculated t-value is greater than the table value at a significance level of 5%.

If it is less than alpha, you reject the null hypothesis. Both are used in hypothesis testing where we are trying accept or reject a given hypothesis. We use them to help us decide if a regression model is “good” or if the predictor variables are “significant.” we use them to help us conclude if data comes from a specific distribution. Or we use them to decide if two processes operate at the same average or the same variation. Or we use them to determine what variables in an experimental design have an impact on the response variable.

Therefore, it is safe to reject the null hypothesis that there is no difference between means. The population set has intrinsic differences, and they are not by chance. The second approach of hypothesis rejection region is the probability value approach.

This publication examined how to interpret alpha and the p-value. Alpha, the significance level, is the probability that you will make the mistake of rejecting the null hypothesis when in fact it is true. The p-value measures the probability of getting a more extreme value than the one you got from the experiment. If the p-value is greater than alpha, you accept the null hypothesis.

This month’s publication looks at alpha and the p-value. Statistical hypotheses are tested using a four-step process. The first step is for the analyst to state the two hypotheses so that only one can be right. The next step is to formulate an analysis plan, which outlines how the data will be evaluated.

The third step is to carry out the plan and physically analyze the sample data. The fourth and final step is to analyze the results and either reject the null hypothesis, or claim that the observed differences are explainable by chance alone. A random sample of 100 coin flips is taken, and the null hypothesis is then tested.