P-value

P-value

The P-value or probability value is a statistical concept that measures the strength of evidence in support of a scientific theory. It is defined as the probability of obtaining a result equal to or "more extreme" than what was actually observed, assuming that the null hypothesis is true.<ref>

P Values(link). {{{website}}}. StatsDirect.

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Definition

The P-value is used in the context of a null hypothesis testing procedure to quantify the idea of statistical significance of evidence. The null hypothesis is the default assumption that there is no relationship between two measured phenomena.<ref>

P Values(link). {{{website}}}. StatsDirect.

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Interpretation

The P-value is not the probability that the null hypothesis is true, nor is it the probability that the alternative hypothesis is false. It is the probability of observing a result at least as extreme as the one that was actually observed, assuming that the null hypothesis is true.<ref>

P Values(link). {{{website}}}. StatsDirect.

</ref>

Misunderstandings

There are common misunderstandings about the P-value, such as the belief that it is the probability that the observed data were produced by random chance alone. This is not true; the P-value is conditioned on the null hypothesis being true, and hence, it is a measure of compatibility between the observed data and what we would predict or expect to see if the null hypothesis were true.<ref>

P Values(link). {{{website}}}. StatsDirect.

</ref>

See also

• Statistical significance
• Null hypothesis
• Type I and type II errors
• Statistical hypothesis testing

References

<references />

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