An essay on hypothesis testing can be used primarily in decision making using data. The data can be from controlled experiments or observational studies. Hypothesis testing essays discuss tests of significance. When writing an essay on hypothesis testing, data should be carefully collected and the actual test conducted. When the test is run, a given value (p) will result which represents the probability. This value is used to determine if sufficient proof exists to cast-off the null hypothesis. The null hypothesis cannot be accepted but the only failure can be made in rejecting it. Performance of validity and reliability tests must be carefully done when writing hypothesis testing essays. In these cases, the truth is not known.

The null and alternative hypotheses should be stated. The assumptions made about the sample when doing the test must be valid. A suitable test for the assumption thus needs to be chosen. A distribution of the test statistic is derived. When the statistical hypothesis is confirmed, that is based on the null hypothesis entirely, a critical region must be determined. The test result can force us to reject the null. In such a case, the alternative has to be accepted.

This testing of hypotheses involves fundamental assumptions that have to be adhered to for the test to work effectively and obtain good results in the end. An essay on hypothesis testing is based on probability. As such, it is used to determine if two samples are significantly different from the other. Then accurate and complete interpretations and generalizations may be carried out. This type of essay on hypothesis testing has played a major role in statistics as a whole. Significance testing is the favored tool in some, if not most, experimental sciences and will commonly be found in a statistics essay.

## Hypothesis Testing essay example

Hypothesis testing is a decision making process where a certain claim on a population is evaluated. In hypothesis testing, population is defined and a certain claim made in relation to the population. In hypothesis testing, a significance level is determined, a sample taken, then data relating to the sample is collected and calculation are made for testing. The end results of the calculation are a conclusion. Hypothesis testing involves the creation of null hypothesis and alternative hypothesis. It involves parameters such as standard deviation, means, and proportions. We have a number of tests such as T-test, Z-test, F-test, and Chi-test.

The first step in construction of this decision rule is the stating the statistical hypothesis. This involves the identification and decision of null and alternative hypotheses and final decision on an appropriate level of significance. Ho is the null type while H1 is the alternative. Since the interest is in making inference from a given sample information to population parameters, the hypotheses are therefore stated in terms of population parameters on which the inference is to be made. Two forms of the hypothesis are used which are set up to be mutually exclusive and exhaustive of the possibilities.

This step is important because it uses the mathematics behind the statistical test as a basis for testing the specific hypothesis. The relationship of variables is measured null analysis without taking into consideration of numerical levels involved in the analysis.

In this case a decision has to be made using the results obtained. The decision is reject or accept the null hypothesis. The critical values are compared with calculated values, depending on the test to carry out that is F test, chi- test, t test and z test . however before carrying out the tests the normality of the data is determined. If the data is normally distributed then test such as run tests are carry out.