HYPOTHESIS TESTING, WHY WE USE IT AND WHEN WE USE IT.

The practice of hypothesis testing defines its basic structure and its importance together with its appropriate application times.

Hypothesis testing is a fundamental statistical method used to make data-driven decisions and inferences about a population based on sample data. It helps researchers and analysts determine whether an observed effect is statistically significant or if it occurred due to chance.

The purpose of hypothesis testing exists because of two main reasons. **

1. Decision-Making: This process allows researchers to base their conclusion on statistical data.
2. The method verifies scientific data through the use of statistical evidence.
3. The methodology allows researchers to determine meaningful differences between datasets implemented within business practices through A/B testing.
4. Quality Control processes detect unusual data points while keeping all operations uniform.

The applications of hypothesis testing occur during three specific situations. **

The comparison of two separate groups occurs to analyze their effectiveness (for instance drugs).

The analysis checks whether the observed average customer satisfaction score exceeds the assumed expectation level.

The testing of relationships between variables serves as its main application point (for example to understand if more advertising budget improves sales performance).

Conclusion

The systematic framework of hypothesis testing represents a fundamental tool used by statisticians which makes conclusions dependable rather than accidental outcomes. Using this approach allows different professional domains including business and medicine to base their choices on data with certainty.