*34,196*pages on

this wiki

Assessment |
Biopsychology |
Comparative |
Cognitive |
Developmental |
Language |
Individual differences |
Personality |
Philosophy |
Social |

Methods |
Statistics |
Clinical |
Educational |
Industrial |
Professional items |
World psychology |

**Statistics:**
Scientific method ·
Research methods ·
Experimental design ·
Undergraduate statistics courses ·
Statistical tests ·
Game theory ·
Decision theory

In statistics, a result is **significant** if it is unlikely to have occurred by chance, given that a presumed null hypothesis is true.

More precisely, in traditional frequentist statistical hypothesis testing, the **significance level** of a test is the maximum probability of accidentally rejecting a *true* null hypothesis (a decision known as a Type I error). The significance of a result is also called its p-value; the smaller the p-value, the more significant the result is said to be.

For example, one may choose a significance level of, say, 5%, and calculate a *critical value* of a statistic (such as the mean) so that the probability of it exceeding that value, given the truth of the null hypothesis, would be 5%. If the actual, calculated statistic value exceeds the critical value, then it is **significant** "at the 5% level". Symbolically speaking, the significance level is denoted by α (alpha).

If the significance level is smaller, a value will be less likely to be more extreme than the critical value. So a result which is "significant at the 1% level" is more significant than a result which is "significant at the 5% level". However a test at the 1% level is more likely to have a Type II error than a test at the 5% level, and so will have less statistical power. In devising a hypothesis test, the tester will aim to maximize power for a given significance, but ultimately have to recognise that the best which can be achieved is likely to be a balance between significance and power, in other words between the risks of Type I and Type II errors. It is important to note that Type I error is not necessarily any worse than a Type II error, and vice versa. The severity of an error depends on each individual case.

If the alternative hypothesis is in fact true, then a sufficiently large sample size is likely to give a highly significant result, even if the difference between the null hypothesis and the alternative hypothesis is very small. The statistical significance of a result is therefore not an indication of how substantial or important the difference is.de:Statistische Signifikanz lt:Reikšmingumo lygmuo nl:Significantie pt:Significância estatística su:Statistical significance zh:显著性差异

This page uses Creative Commons Licensed content from Wikipedia (view authors). |