The same kind of correspondence is true for other confidence levels and significance levels. For

example, a 90 percent confidence level corresponds to the α = 0.10 significance level, and a 99

percent confidence level corresponds to the α = 0.01 significance level, and so on.

So you have two different but related ways to estimate if an effect you see in your sample is a true

effect. You can use significance tests, or else you can use CIs. Which one is better? Even though the

two methods are consistent with one another, in biostatistics, we are encouraged for ethical reasons to

report the CIs rather than the result of significant tests.

The CI around the mean effect clearly shows you the observed effect size, as well as the size of the

actual interval (indicating your level of uncertainty about the effect size estimate). It tells you not

only whether the effect is statistically significant, but also can give you an intuitive sense of

whether the effect is clinically important, also known as clinically significant.

In contrast, the p value is the result of the complex interplay between the observed effect size, the

sample size, and the size of random fluctuations. These are all boiled down into a single p value

that doesn’t tell you whether the effect was large or small, or whether it’s clinically significant or

negligible.