Hypothesis Testing Homework Help, Hypothesis Testing Assignment Help
Hypothesis Testing is a method in statistics where assumptions regarding a population parameter are tested by an analyst. The test is conducted with the aim of accepting or rejecting the null hypothesis. The method used by an analyst depends on the reason for the research and the type of data used. The analyst states the null hypothesis and the test proves whether it is true or not. If it is not true an alternative hypothesis is formulated to be tested. The analyst carries out this continuous process until a true hypothesis is revealed by the data.
- The null hypothesis- this is the hypothesis that the analyst believes to be true
- Alternative hypothesis- it is the opposite of the null hypothesis, it is considered by the analyst to be untrue.
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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