Wrap-Up (Inference for One Variable)
We’ve now completed the two main sections about inference for one variable. In these sections we introduced the three forms of inference:
- Point estimation—estimating an unknown parameter with a single value
- Interval estimation—estimating an unknown parameter with a confidence interval (an interval of plausible values for the parameter, which with some level of confidence we believe captures the true value of the parameter in it).
- Hypothesis testing — a four-step process in which we are assessing the statistical evidence provided by the data in favor or against some claim about the population.
Much like in the Exploratory Data Analysis section for one variable, we distinguished between the case when the variable of interest is categorical, and the case when it is quantitative.
- When the variable of interest is categorical, we are making an inference about the population proportion (p), which represents the proportion of the population that falls into one of the categories of the variable of interest.
- When the variable of interest is quantitative, the inference is about the population mean (μ, mu).