In previous modules, we learned to make inferences about a population proportion. In particular, we learned the following:

• Random samples vary. When we use a sample proportion to make an inference about a population proportion, there is uncertainty. For this reason, inference involves probability.
• Under certain conditions, we can model the variability in sample proportions with a normal curve. We use the normal curve to make probability-based decisions about population values.
• We can estimate a population proportion with a confidence interval. The confidence interval is an actual sample proportion with a margin of error. We state our confidence in the accuracy of these intervals using probability.
• We can test a hypothesis about a population proportion using an actual sample proportion. Again, we base our conclusion on probability using a P-value. The P-value describes the strength of our evidence in rejecting a hypothesis about the population.

In Inference for Two Proportions, we continue to work with categorical data, so we continue to use proportions. But now we make inferences that compare two populations (or two treatments).

As an overview, consider again the Big Picture of Statistics.

Here we discuss the four steps in a statistical investigation for situations from Module 9.

1. Produce Data: Determine what to measure, then collect the data. In this module, we collect categorical data from two samples. In an observational study, we begin with two populations and randomly select a sample from each population. In an experiment, we randomly assign individuals to two treatments. The use of random selection or random assignment allows us to view the samples as independent. This means we assume that the variable values from one sample do not influence the values for the other sample.
2. Exploratory Data Analysis: Analyze and summarize the data. We are working with categorical data, so from each sample, we compute a sample proportion. To compare the two samples, we subtract the proportions. When we conduct inference in the next step, our goal is to to determine if the actual difference in the sample proportions is significantly different from what we expect in random sampling.
3. Draw a Conclusion: Use data, probability, and statistical inference to draw a conclusion about the populations.Our approach to inference repeats the reasoning we did in Inference for One Proportion.
   • We use simulation to observe the behavior of the differences in sample proportions when we randomly select many, many samples. We create the simulation to reflect a claim about the populations. Then we develop a probability model to describe the shape, center, and spread of the sampling distribution. Of course, we are interested in the conditions that allow us to use a normal curve.
   • We use this model to determine when a given difference is unusual in a formal hypothesis test.
   • We also construct confidence intervals to estimate the difference between two population proportions. As before, we make a probability statement about our confidence in the accuracy of these intervals.