Question

Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research. Provide a workplace example that illustrates your ideas.

Answer 1

In this series of posts, I show how hypothesis tests and confidence intervals work by focusing on concepts and graphs rather than equations and numbers.

Previously, I used graphs to show what statistical significance really ,means In this post, I’ll explain both confidence intervals and confidence levels, and how they’re closely related to P values and significance levels.

How to Corristectly Interpret Confidence Intervals and Confidence Levels

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.

A 95% confidence interval indicates that 19 out of 20 samples (95%) from the same population will produce confidence intervals that contain the population parameter.

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.