6-1 Journal: Rejecting and Failing to Reject the Null Hypothesis

6-1 Journal – Rejecting and Failing to Reject the Null Hypothesis
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April 16, 2017
Hypothesis testing, or test of statistical significance, is perhaps one of the most important and commonly used test in the field of statistics. This importance is due to the fact that in real-life applications (and theoretical) variables with different values, where one is higher than the other, is easy to accept as a general fact. This then could lead to an error or even worse, if these superficial data would not be tested based on their ‘statistical significance’ CITATION Taynd \l 1033 (Taylor, n.d.). therefore, statistics make it hypothesis testing as one of their main tools in order to prevent this problem. In doing hypothesis testing a ‘null hypothesis’ must first be assumed. This null hypothesis means that there is not change, or that hypothesis is true. However, when sufficient conditions are met, we either reject the null (which leads to the alternative hypothesis) or fail to reject the null hypothesis (which means that the data is statistically significant) CITATION stand \l 1033 (statisticshowto.com, n.d.). However, the tools that we use to reject the null is not 100% fool-proof. Thus, there are plenty of cases where the null was either rejected or accepted while they are actually opposite of what they were thought to be. Because of this, the concept of ‘errors’ were created. This error could...