Confidence tests and hypothesis tests both form the foundation of statistics.

The confidence interval holds importance in research to offer a strong base for research estimations, especially in medical research. The confidence interval provides a range of values that helps in capturing the unknown parameter.

Hypothesis testing is used to test an experiment or observation and determine if the results did not occur purely by chance or luck using the below formula where ‘p’ is some parameter.

Confidence and hypothesis testing are inferential techniques used to either estimate a parameter or test the validity of a hypothesis using a sample of data from that data set. While confidence interval provides a range of values for an accurate estimation of the precision of that parameter, hypothesis testing tells us how confident we are inaccurately drawing conclusions about a parameter from a sample. Both can be used to infer population parameters in tandem.

In case we include 0 in the confidence interval, it indicates that the sample and population have no difference. If we get a p-value that is higher than alpha from hypothesis testing, it means that we will fail to reject the bull hypothesis.