A test statistic is used to measure and examine a random sample of the population being put under analysis. This makes it useful in a hypothesis test because it assists in the determination and coming up with a concrete decision to support or reject the null hypothesis. This takes into consideration factors such as hypothesis testing which is also known as the test of significance. Barsalou and Smith (2018) say that hypothesis testing is a method for testing a claim or hypothesis about an argument in a population, using data measured in a particular sample. It is therefore very prudent that people understand why a researcher needs to calculate a test statistic.
Calculating a test statistic allows the researcher to have a wider range of thoughts and come up with a concrete conclusion that has factual support through a better understanding of statistics. This is due to their ability to understand how statistics work before they come to any conclusive argument of either supporting or rejecting a hypothesis. As a result, it becomes a lot easier for them to interpret the statistics and come up with a meaningful argument that can be share to other individuals. A test result will influence the researcher’s meaning to a hypothesis and hence greatly impact other people to either accept or reject the test of significance as seen by Urdan (2016).
A test statistic will allow the researcher to know how far, or how many standard deviations, a sample mean is from the population mean. If the value of the test statistic is very large, the distance is likely to be further. This can also show the number of standard deviations that a sample mean is from the population mean stated in the null hypothesis. Consequently, it makes a test statistic very significant in any research because the value of the test statistic is used to make a comprehensive decision (Hinton & McMurray,2017).
In conclusion, a test statistic is a mathematical formula used by researchers to determine the probability of obtaining sample outcomes in relation to the null hypothesis. It is important because it influences the kind of decision a researcher will take. It also assists in the determination of the extent of a deviation existing between the sample and population means.
Barsalou, M. A., & Smith, J. (2018). Applied Statistics Manual: A Guide to Improving and Sustaining Quality with Minitab. Welshpool. Quality Press.
Hinton, P. R., & McMurray, I. (2017). Presenting your data with SPSS explained. Oxfordshire . Taylor & Francis.
Urdan, T. C. (2016). Statistics in plain English. Oxfordshire. Taylor & Francis.