Question

P- values and confidence intervals are both used in hypothesis testing. explain three reasons why it may be preferable to report a confidence interval over a P-value. Provide a specific example to justify the reasons.

Answer 1

what is P -value?

P -value is defined as under the assumption that null hypothesis is correct, p value is the probability is finding test result at least as extreme as the actual result. It helps to determine the significance of your findings in relation to null hypothesis. If the p value is less than or equal to 0.05, it has statistical significance. Based on the p - value resercher can reject or maintain the previously formed null hypothesis.

What is confidence interval?

Confidence interval are used to express the degree of uncertainty about the sample statistic. It is defined as a set of values the researcher sure their true values lie in.

REASONS TO PREFER CONFIDENCE INTERVAL OVER A P -VALUE

1. Confidence interval gives data about a range in which the true value lies with a certain degree of probability. for Example, if we select 40 adults randomly and checked the height, mean height is 175 cm and the standard deviation is 20 cm. Then the confidence interval is 175±6.2cm, this mean we can expect the true mean ranges from 168.8 to 181.2cm.

2. The benefit of confidence interval over p value is it reflect the results at the level of data measurement. Higher the sample size broader the confidence interval.

3. It also help to give the direction of the studied effect. It helps to determine the statistical significance of the study. For example, a study is conducted on a drug and its effect on blood pressure, two treatment groups are studied and the question is whether the value 0 mm Hg is within the 95% confidence interval or outside. If the confidence interval includes the relative risk 1.00, then the result is not significant

These are the reasons to prefer confidence interval over a p value.