A Hypothesis test is conducted in a four step process;
- The analyst states two hypotheses where only one can be true
- An analysis plan is then formulated which determines how data will be evaluated
- The analysis plan is implemented and a physical analysis of sample data is carried out
- The results obtained are then analysed and the null hypothesis is either accepted or rejected.
An analyst uses P-value in hypothesis testing as an alternative to rejection points, to provide the least level of significance at which the null hypothesis would be rejected. P-value is the level of marginal significance in a test which shows the likelihood of a given event occurring. When the p-value is smaller, stronger evidence favours the alternative hypothesis.
Type II error occurs when an analyst accepts a null hypothesis that is actually untrue. It fails to either reject or accept the null hypothesis despite the alternative hypothesis being true. Type I error on the other hand occurs when a null hypothesis is rejected although it is true. It accepts the alternative hypothesis although it occurs by chance.
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