A confidence interval is a range of values that is likely to contain an unknown population parameter. If you draw a random sample many times, a certain percentage of the confidence intervals will contain the population mean. This percentage is the confidence level. Most frequently, you’ll use confidence intervals to bound the mean or standard deviation, but you can also obtain them for regression coefficients, proportions, rates of occurrence (Poisson), and for the differences between populations. Just as there is a common misconception of how to interpret P values, there’s a common misconception of how to interpret confidence intervals. In this case, the confidence level is not the probability that a specific confidence interval contains the population parameter. The confidence level represents the theoretical ability of the analysis to produce accurate intervals if you are able to assess many intervals and you know the value of the population parameter. For a specific confidence interval from one study, the interval either contains the population value or it does not—there’s no room for probabilities other than 0 or 1. And you can't choose between these two possibilities because you don’t know the value of the population parameter. Therefore, I choose confidence intervals (CI) because you can use either P values or confidence intervals to determine whether your results are statistically significant. If a hypothesis test produces both, these results will agree. The confidence level is equivalent to 1 – the alpha level. So, if your significance level is 0.05, the corresponding confidence level is 95%.- If the P value is less than your significance (alpha) level, the hypothesis test is statistically significant.- If the confidence interval does not contain the null hypothesis value, the results are statistically significant.- If the P value is less than alpha, the confidence interval will not contain the null hypothesis value.