Statistical significance is widely considered to have been achieved when the probability of a result occurring by chance is equal to or less than 5% (P ≤ 0.05). Higher levels of significance are P ≤ 0.01 (less than 1%) and P ≤ 0.001 (less than 0.1%).
These levels of significance are also sometimes referred to as significant at the 5%, 1% and 0.1% levels, respectively, but it is much better to give the P values as indicated above.
P values greater than 0.05 are generally considered to be ‘not significant’. However, in some disciplines, a different cut-off may be used (eg 0.1, or 10%).
Since P is a measure of probability, its value must lie between 0 and 1. Some statistical software programs report the exact value of P, and may round a very small value (eg 0.00001) to 0.000. This should be reported as P ≤ 0.001.
Sometimes the ‘equals’ is omitted, and the value is given as P < 0.01; strictly speaking, the symbol should be ‘≤’.
Do not use wording such as A was greater than B; however, the difference was not statistically significant. If the difference was not statistically significant, A cannot be said to be greater than B.
It is usually best to quote a precise P value. This permits readers to assess a statistical result individually. For example, the P values associated with the main results of your study might be P = 0.057 and P = 0.57. Although you could report both these as P > 0.05 or P = NS (not significant), you can only report that the 2 results differ if you provide the precise values.
In tables or figures, levels of statistical significance are often denoted by a system of symbols. For clarity, these should be defined under the table or figure in terms of P values:
* P ≤ 0.05
** P ≤ 0.01
*** P ≤ 0.001
However, P values are just one aspect of quantitative statistical information. A discussion of variability and uncertainty may also benefit from presenting standard deviations and confidence intervals (see Presentation of uncertainty).