Testing of Hypothesis
The study hypothesis has been expressed as a null and alternative hypothesis in two separate ways. They show the results or variations in a given population. In the majority of cases, the null hypothesis is tested. The test shows how the entire population is situated (Wilcox, 2017). The null hypothesis basically implies zero difference or effect and is usually equated to zero. It assumes that a test did not take place while the alternatives attempt to show otherwise (s). There are only two possible outcomes when hypothesis test is undertaken; it is either rejecting the null hypothesis or failing to reject the null hypothesis. The former only concludes that there is sufficient information or evidence that at a certain given percentage of the confidence interval, the null hypothesis is true. The later only proves that there is inadequate evidence to prove that the null is not true (false) and at a certain given percentage of the confidence interval (Wilcox, 2017). When the null hypothesis is failed to be rejected, it does not mean that null is true since hypothesis testing only tries to find if there is evidence exists and are sufficient to reject the null hypothesis. A hypothesis test does not evaluate whether the claims are true or are most likely to be true. They are therefore not the same. An example would be that an accused person before the court of law is not guilty until proven so.
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A failed t-test does not prove that null hypothesis is true. If the p-value (from the t-test) given is less or equal to one given in problem statement (which is α), then the null hypothesis is rejected while the otherwise of it is meant to not rejecting it (Wilcox, 2017).
It is true that the claim should always be written as the alternative hypothesis since it shows the effect/difference that occurs in the given tested data. An example would be (Ho and H1 denotes the null and alternative hypothesis respectively);
Ho: Undertaking training does not affect the performances of employees.
H1: Undertaking training has a negative effect on the performances of employees.
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Reference
Wilcox, R. R. (2017). Introduction to robust estimation and hypothesis testing. Cambridge, MA: Academic Press.
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