Interpreting Confidence Intervals

Quantitative Results

Confidence intervals are often included in the output of a statistical analysis. However, they can be difficult to understand and interpret. A confidence interval is an indication of a population’s parameters. This means that confidence intervals are an estimate of how certain or uncertain the estimated difference between variables is. A narrower CI indicates a more precise estimate, while a wider CI indicates a less precise estimate.

Additionally, confidence intervals can be used as another method of determining significance. Therefore, if the 95% confidence interval contains the value of zero, then the p value will be greater than .05. This is also true if the CI crosses through zero, meaning that the interval ranges from a negative number to a positive number.  However, if your CI does not contain or cross through zero (meaning the numbers go from a positive number to another positive number or from a negative number to another negative number), then the p value will be less than .05.  

Finally, confidence intervals can be evaluated at the 90%, 95%, and 99% level. While a 95% CI is the commonly used measure, CI depends on the confidence that the researcher wishes to have for their estimate. Because of this, the choice to use a 90 or a 99% is up to you as the researcher. Along with confidence, a CI is also related to the size of the sample, meaning that the larger the sample size is in a given experiment, the more narrow a confidence interval is.

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