How to test hypothesis using z-test and t-test by application of distribution table
How to run z-test and t-test hypothesis testing for measuring differences. This article discusses the continuation of the hypothesis testing for data analysis. The use of the hypothesis testing is crucial in determining the perception of the assumption. If the assumption was rejected for the null hypothesis, then we can have enough evidence to “accept” the alternative or research hypothesis that we registered for our study. This article also discusses the rejection region that is set by the researchers.
We also discuss the use of the formula for the analysis for the z score. This z score than we compared with the z-normal tabulation curve. If the calculation is AWAY from our setting up region, then we REJECT the NULL hypothesis. The use of the t-test and z-test was primarily to test the significant differences between the mean of samples collected with the standard.
This can help the researcher to produce valid interpretation and judgment not only in a hypothetical manner but also contribute to the scientific evaluation of the data that previously collected. This video, further explains the z-test application and the distribution in hypothesis testing.
Run the z-test and t-test hypothesis testing for measuring differences