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The concept of confidence intervals is general. You can calculate the 95% CI for almost any value you compute when you analyze data. We've already discussed the CI of a SD. Other confidence intervals include:
| • | The difference between two group means |
| • | The ratio of two proportions |
| • | The best-fit slope of linear regression |
| • | The best-fit value of an EC50 determined by nonlinear regression |
| • | The ratio of the median survival times of two groups |
The concept is the same for all these cases. You collected data from a small sample and analyzed the data. The values you compute are 100% correct for that sample, but are affected by random scatter. A confidence interval tells you how precisely you have determined that value. Given certain assumptions (which we list with each analysis later in this book), you can be 95% sure that the 95% CI contains the true (population) value.
The fundamental idea of statistics is to analyze a sample of data, and make quantitative inferences about the population from which the data were sampled. Confidence intervals are the most straightforward way to do this.
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