- It evaluates two mutually exclusive statements about a population to determine which statement is best supported by the sample data.
- You say an average student in the class is 30 or a boy is taller than girls.
- All those are an example in which we assume or need some statistic way to prove those.
- We need some mathematical conclusion whatever we are assuming is true.
- 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.
- 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.
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 \alpha 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.
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.