Hypothesis Testing and Confidence Intervals

Hypothesis Testing and Confidence Intervals
Name
Institution
Course
Professor
Date
Hypothesis Testing and Confidence Intervals
Hypothesis testing refers to the use of data to come up with conclusions about a particular sample of a population under study. The importance of such testing is to determine the likelihood that a chosen parameter can be true. Therefore, in instances where there are opposing claims, undertaking hypothesis testing ensures the validity and reliability of such claims, thus ensuring that their strength is determined before making a decision (Emmert-Streib & Dehmer, 2019). On the other hand, confidence intervals are important in statistics since there is uncertainty whenever a sample in a population is used. Consequently, a confidence level provides a range of values in which the statistical estimate can fall with each experiment undertaken (Emmert-Streib & Dehmer, 2019). Therefore, hypothesis testing and confidence levels are important tools during the research process and help in understanding the results before making essential decisions.
When undertaking hypothesis testing, the first step involves the determination of the null and alternative hypotheses. This means that to prove one claim, there has to be a counterclaim that has been disapproved hence the need for at least two hypotheses to be tested. The null hypothesis (H0) is an assumption made by the researcher to show that there were no changes or that the claim being made is false (Verma & Abdel-Salam, 2019). For instance, H0 can be determined that a subgroup reacts positively to a treatment regimen. On the other hand, the alternative hypothesis (Ha) counters the null hypothesis by suggesting that the claim is true or plausible (Verma & Abdel-Salam, 2019). From the above example, Ha can be determined that subgroups react differently to a treatment regimen. After t...