Hypothesis Testing
Hypothesis Testing
The process of hypothesis testing is to draw inferences or some conclusion about the overall population or data by conducting some statistical tests on a sample.
Basically we have two types of Hypothesis
1) Null Hypothesis
2) Alternate Hypothesis
Null Hypothesis : Its a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations. In Simple terms , Group proportions are equal in the population p1 = p2.
Alternate Hypothesis : It’s a type of statistical hypothesis which is contradictory to the Null Hypothesis. Alternative Hypothesis states that a population parameter does not equal a specified value i.e., p1 != p2.
In order to make a decision whether to reject the null hypothesis a test statistic is calculated.
We have two types of approaches
1. Critical Value Approach
2. P – Value Approach
The Critical Value Approach : By applying the critical value approach it is determined, whether or not the observed test statistic is more extreme than a defined critical value. Therefore the observed test statistic (calculated on the basis of sample data) is compared to the critical value, some kind of cutoff value. If the test statistic is more extreme than the critical value, the null hypothesis is rejected. If the test statistic is not as extreme as the critical value, the null hypothesis is not rejected. The critical value is computed based on the given significance level αα and the type of probability distribution of the idealized model. The critical value divides the area under the probability distribution curve in rejection region(s) and in non-rejection region.
The P-Value Approach : The P-value method is used in Hypothesis Testing to check the significance of the given Null Hypothesis. Then, deciding to reject or support it is based upon the specified significance level or threshold.
If the p-value is less than or equal to the specified significance level αα, the null hypothesis is rejected; otherwise, the null hypothesis is not rejected. In other words, if p≤αp≤α, reject H0, otherwise, if p>αp>α do not reject H0.
Types of Error :
· Type | Error
· Type || Error
Type | Error (False Positive): A Type I error means rejecting the null hypothesis when it’s actually true. It means concluding that results are statistically significant when, in reality, they came about purely by chance or because of unrelated factors.
Type || Error (False Negative) : A type II error is a statistical term used within the context of hypothesis testing that describes the error that occurs when one fails to reject a null hypothesis that is actually false. A type II error produces a false negative, also known as an error of omission.