Significance levels

The significance level is the criterion used for rejecting the null hypothesis.

Use as follows:

• determine the difference between the results of the experiment and the null hypothesis
• compare the probability of the null hypothesis to the significance level

If the probability is less than or equal to the significance level, then the null hypothesis is rejected and the outcome is said to be statistically significant.

Traditionally, researchers have used either the 0.05 level (5% level) or the 0.01 level (1% level), although the choice is largely subjective. The lower the significance level, the more conservative the statistical analysis and the more the data must diverge from the null hypothesis to be significant.

Star shorthand for significance levels

In publications significance levels are often indicated with stars. However, there is no consistent convention

star code	significance	comment
***	0.01	high significance
**	0.05	medium significance
*	0.10	low significance

Figure 3d from Tanabe et al. 2008 showing data sets that are deemed not significantly different (N.S.; >0.05) and data sets that are significant at different levels: * p<0.05, ** p<0.01, *** p<0.001

Figure 5b from Wang et al. 2004 showing a different star code for significance levels. Note how the significance decreases with a wider data point distribution at 22 vs. 10 days.