Confidence Interval Is Preferable Over P Values

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P-values are the same as a significance level or alpha level and it is the probability of a type 1 error occurring. A type 1 error is when the null hypothesis is true but is rejected. Therefore, type 1 error is rejecting something that is true. Whereas, the confidence interval illustrates a range around the estimate of value (Corty, 2016). For example, the upper and lower confidence interval is 22-34 and the value is 29, therefore, the value lies within the estimated range.

Three reasons why the confidence interval is preferable over p-values.

The confidence intervals depict a precise, yet easier to interpret a range for the possible true value that relates to the measurement. Whereas, a p-value can only give you a point estimate of the best guess of what the value could be. For example, a drug was created that increases blood pressure. If a researcher was to ask how high the drug increases the blood pressure without any analysis, the p-value would state 36 and the confidence interval would be a range of 30-40. It would be harder for the researcher to guess the exact point estimate of 36, but easier to use the interval estimate.

The confidence interval value provides more information on the data, such as the precision, statistical significance, clinical relevance, direction, and strength of the effect. Whereas, the p-value provides only provide statistical significance, does not give a direct statement about the direction, provides less information, and provides no information on clinical relevance (du Prel, et al., 2009; Gupta, 2012).

P-values can lead to error, because of its binary effect when hypothesis testing due to a single value (du Prel, et al., 2009). For example, if the p-value results in the value 25, an estimate of either yes and no will be the decision that will decide whether the hypothesis is true or not, which leaves room for errors. These errors include type 1 and 2 error, where type 1 error is rejecting a null hypothesis that is true and a type 2 error is accepting a null hypothesis that is false (Corty, 2016). Whereas, confidence interval provides ranges that will cover a wider scope of real values and not just a single point estimate value.

Reference

Corty, E. (2016). Using and interpreting statistics: A practical text for the behavioral, social, and health sciences (3rd ed.). New York, NY: Macmillan Learning.

Du Prel, J. B., Hommel, G., Röhrig, B., & Blettner, M. (2009). Confidence interval or p-value?: part 4 of a series on evaluation of scientific publications. Deutsches Arzteblatt international, 106(19), 335–339. Doi: 0.3238/arztebl.2009.0335

Gupta S. K. (2012). The relevance of confidence interval and P-value in inferential statistics. Indian journal of pharmacology, 44(1), 143–144. Doi: 10.4103/0253-7613.91895

 

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