A hypothesis is a valuable component of a scientific research project. The hypothesis is the explanation regarding a phenomenon under investigation. In spite of their significance, hypotheses can only qualify to be used in a scientific analysis if they meet a certain criterion attained through testing. Hypothesis testing is a procedure that uses probability theory and sample evidence to determine whether a proposed statement on a subject matter should (or should not) be rejected. Hypothesis testing constitutes five steps with a difference between one-sample and two sample tests.
The first step involves stating the null and alternate hypotheses. Here, the null hypothesis refers to an explanation showing no relationship between variables in research. The alternate hypothesis, as the name suggests, is a statement accepted if evidence falsifies the null hypothesis (Cho & Abe, 2013). Having done that, the next step involves selecting an appropriate test statistic and significance level. In statistics, z-statistic/z-test formulas are used for testing the hypothesis of a proportion and t-statistic formula for the hypothesis of a mean.
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Third, one should state and clarify the decision rules. The decision rules outline conditions for accepting or rejecting a null hypothesis. The level of significance determined in the previous step helps find the critical value; one that separates the reject region from the non-reject regions (Cho & Abe, 2013). In the next step, one computes the appropriate test statistic and makes the decision. The test can either be z-statistic or t-statistic. Upon doing that, the author should compare the computed test statistic with the critical value to determine whether the null hypothesis should be rejected or not.
In the final step, five, a conclusion is made based on the decision in step four. The conclusion is made in the context of the original problem. Here, a person interprets the numerical value obtained, and its meaning on the rejection (or approval) of a null hypothesis (Cho & Abe, 2013). In case a statement does not meet the stated criterion, it is considered invalid and thus cast aside.
One-sample and two-sample tests are two themes in research that are often confused, or mistaken for one another. The two concepts differ substantially: According to Chwialkowski, Ramdas, Sejdinovic, and Gretton (2015), while a one-sample test is used for the comparison of a mean that is known, in most cases zero (0), a two-sample is used to compare the means of two or more different samples.
References
Cho, H. C., & Abe, S. (2013). Is two-tailed testing for directional research hypotheses tests legitimate?. Journal of Business Research , 66 (9), 1261-1266.
Chwialkowski, K. P., Ramdas, A., Sejdinovic, D., & Gretton, A. (2015). Fast two-sample testing with analytic representations of probability measures. In Advances in Neural Information Processing Systems (pp. 1981-1989).
