Today’s class we gone through a P-Value and Hypothesis testing
P-value:
The P-value is known as the probability value. It is defined as the probability of getting a result that is either the same or more extreme than the actual observations.
The P-value is known as the level of marginal significance within the hypothesis testing that represents the probability of occurrence of the given event.
The P-value is used as an alternative to the rejection point to provide the least significance at which the null hypothesis would be rejected. If the P-value is small, then there is stronger evidence in favour of the alternative hypothesis.
Hypothesis Testing :
Usually, we get Sample Datasets to work on and perform data analysis and visualization and find insights.
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.
Based on that probability and a significance level, we reject or fail to reject the Null Hypothesis.
Generally, the lower the p-value, the higher the chances are for Rejecting the Null Hypothesis .