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LogoutThe hypothesis test is aimed to test if the null hypothesis should be rejected in favor of the alternative hypothesis. The basic logic of a hypothesis test is to compare two statistical data sets. One data set is obtained by sampling and the other data set originates from an idealized model.
The purpose of hypothesis testing is to test whether the null hypothesis (there is no difference, no effect) can be rejected or approved. If the null hypothesis is rejected, then the research hypothesis can be accepted. If the null hypothesis is accepted, then the research hypothesis is rejected.
Parameters of hypothesis testing
- Null hypothesis(H0): In statistics, the null hypothesis is a general given statement or default position that there is no relationship between two measured cases or no relationship among groups.
- In other words, it is a basic assumption or made based on the problem knowledge. Example: A company production is = 50 unit/per day etc.
- Alternative hypothesis(H1): The alternative hypothesis is the hypothesis used in hypothesis testing that is contrary to the null hypothesis. Example : A company production is not equal to 50 unit/per day etc.
- Level of significance: It refers to the degree of significance in which we accept or reject the null-hypothesis. 100% accuracy is not possible for accepting a hypothesis, so we, therefore, select a level of significance that is usually 5%. This is normally denoted with and generally, it is 0.05 or 5%, which means your output should be 95% confident to give similar kind of result in each sample.
- P-value: The P value, or calculated probability, is the probability of finding the observed/extreme results when the null hypothesis(H0) of a study given problem is true. If your P-value is less than the chosen significance level then you reject the null hypothesis i.e. accept that your sample claims to support the alternative hypothesis.