Simple Hypothesis: A statistical hypothesis which specifies the population completely (i.e. the form of probability distribution and all parameters are known) is called a simple hypothesis. 1. Composite Hypothesis: A statistical hypothesis which does not specify the population completely (i.e. either the form of probability distribution or some parameters remain unknown) is called a Composite Hypothesis.
Hypothesis Testing or Test of Hypothesis or Test of Significance Hypothesis Testing is a process of making a decision on whether to accept or reject an assumption about the population parameter on the basis of sample information at a given level of significance.
Null Hypothesis: Null hypothesis is the assumption which we wish to test and whose validity is tested for possible rejection on the basis of sample information.
It asserts that there is no significant difference between the sample statistic (e.g. Mean, Standard Deviation(S), and Proportion of sample (p)) and population parameter (e.g. Mean(µ), standard deviation (σ), Proportion of Population (P)).
Symbol-It is denoted by Ho
Acceptance- The acceptance of null hypothesis implies that we have no evidence to believe otherwise and indicates that the difference is not significant. Rejection- The rejection of null hypothesis implies that it is false and indicates that the difference is significant.
Alternative Hypothesis: Alternative hypothesis is the hypothesis which differs from the null hypothesis. It is not tested. Symbol-It is denoted by H1.
Acceptance- its acceptance depends on the rejection of the null hypothesis. Rejection- Its rejection depends on the acceptance of the null hypothesis.
Level of Significance
Level of significance is the maximum probability of rejection the null hypothesis when it is true.
Symbol- it is usually expressed as % and is denoted by symbol α (called Alpha) Example- 5% level of significance implies that there are about 5 chances in...
Please join StudyMode to read the full document