Test statistics are statistics such as t and F which may have run across in the past. In more difficult issues of testing however there will occur a variety of apparently reasonable and essentially varied test statistics and then it becomes important to select one of them. The major criteria of option is the resulting test power if one statistic offers greater power than another when both are applied at the same significance level and most probably the powerful statistic is prescribed which results in a smaller probability of false acceptance (Hodges and Lehmann, 2004, p 393).
In hypothesis testing the decision can be made by predicting rejection region which is the region of possible values for the test statistics that may lead to null hypothesis rejection. If the test statistics is in the rejection region then the result is significant statistically and it rejects the null hypothesis or it does not reject the null hypothesis. The rejection region alters relying on the degree of freedom accompanying the test (Utts and Heckard, 2011, p 506).
Unlike qualitative research that is descriptive in nature, quantitative research relies upon some form of inference. Statistical analysis is a technique that is used to quantify the confidence of the inferences made from a quantitative analysis. In order to understand in detail about statistical analysis it is important to be aware of certain terminologies associated with statistical analysis.