** Boost your Grades with us today! **

## DNP 820 Topic 6 Discussion Question One

*DNP 820 Topic 6 Discussion Question One*

There is a heavy focus on achieving statistical significance when evaluating outcomes. Often in research or EBP projects, there is no statistical significance, only possible clinical significance. When is it appropriate to deem a project’s outcomes successful only using clinical significance as the only measure of success?

In statistical hypothesis testing,^{[1]}^{[2]} a result has **statistical significance** when it is very unlikely to have occurred given the null hypothesis.^{[3]}^{[4]} More precisely, a study’s defined **significance level**, denoted by {\displaystyle \alpha }, is the probability of the study rejecting the null hypothesis, given that the null hypothesis was assumed to be true;^{[5]} and the *p*-value of a result, *{\displaystyle p}*, is the probability of obtaining a result at least as extreme, given that the null hypothesis is true.^{[6]} The result is **statistically**

**significant,** by the standards of the study, when {\displaystyle p\leq \alpha }.^{[7]}^{[8]}^{[9]}^{[10]}^{[11]}^{[12]}^{[13]} The significance level for a study is chosen before data collection, and is typically set to 5%^{[14]} or much lower—depending on the field of study.^{[15]}

In any experiment or observation that involves drawing a sample from a population, there is always the possibility that an observed effect would have occurred due to sampling error alone.^{[16]}^{[17]} But if the *p*-value of an observed effect is less than (or equal to) the significance level, an investigator may conclude that the effect reflects the characteristics of the whole population,^{[1]} thereby rejecting the null hypothesis.^{[18]}

This technique for testing the statistical significance of results was developed in the early 20th century. The term *significance* does not imply importance here, and the term *statistical significance* is not the same as research, theoretical, or practical significance.^{[1]}^{[2]}^{[19]}^{[20]} For example, the term clinical significance refers to the practical importance of a treatment effect.^{}

Statistical significance dates to the 1700s, in the work of John Arbuthnot and Pierre-Simon Laplace, who computed the *p*-value for the human sex ratio at birth, assuming a null hypothesis of equal probability of male and female births; see *p*-value § History for details.^{[22]}^{[23]}^{[24]}^{[25]}^{[26]}^{[27]}^{[28]}

In 1925, Ronald Fisher advanced the idea of statistical hypothesis testing, which he called “tests of significance”, in his publication *Statistical Methods for Research Workers*.^{[29]}^{[30]}^{[31]} Fisher suggested a probability of one in twenty (0.05) as a convenient cutoff level to reject the null hypothesis.^{[32]} In a 1933 paper, Jerzy Neyman and Egon Pearson called this cutoff the *significance level*, which they named {\displaystyle \alpha }. They recommended that {\displaystyle \alpha } be set ahead of time, prior to any data collection.^{[32]}^{[33]}

Despite his initial suggestion of 0.05 as a significance level, Fisher did not intend this cutoff value to be fixed. In his 1956 publication *Statistical Methods and Scientific Inference,* he recommended that significance levels be set according to specific circumstances.^{[32]}

### Related concepts[edit]

The significance level {\displaystyle \alpha } is the threshold for {\displaystyle p} below which the null hypothesis is rejected even though by assumption it were true, and something else is going on. This means that {\displaystyle \alpha } is also the probability of mistakenly rejecting the null hypothesis, if the null hypothesis is true.^{[5]} This is also called false positive and type I error.

Sometimes researchers talk about the confidence level *γ* = (1 − *α*) instead. This is the probability of not rejecting the null hypothesis given that it is true.^{[34]}^{[35]} Confidence levels and confidence intervals were introduced by Neyman in 1937.