Examples of inferential statistics in the following topics:

 Descriptive statistics and inferential statistics are both important components of statistics when learning about a population.
 Descriptive statistics are distinguished from inferential statistics in that descriptive statistics aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent.
 This generally means that descriptive statistics, unlike inferential statistics, are not developed on the basis of probability theory.
 Even when a data analysis draws its main conclusions using inferential statistics, descriptive statistics are generally also presented.
 The use of descriptive and summary statistics has an extensive history and, indeed, the simple tabulation of populations and of economic data was the first way the topic of statistics appeared.


 Although no assumptions were needed to determine the bestfitting straight line, assumptions are made in the calculation of inferential statistics.
 As applied here, the statistic is the sample value of the slope (b) and the hypothesized value is 0.

 Statistics also provides tools for prediction and forecasting.
 Statistical models can also be used to draw statistical inferences about the process or population under study—a practice called inferential statistics.
 Inferential statistics uses patterns in the sample data to draw inferences about the population represented, accounting for randomness.
 Probability is used in "mathematical statistics" (alternatively, "statistical theory") to study the sampling distributions of sample statistics and, more generally, the properties of statistical procedures.
 In descriptive statistics, summary statistics are used to summarize a set of observations, in order to communicate the largest amount as simply as possible.




 Descriptive statistics are numbers that are used to summarize and describe data.
 Any other number we choose to compute also counts as a descriptive statistic for the data from which the statistic is computed.
 Generalizing from our data to another set of cases is the business of inferential statistics, which you'll be studying in another section.
 You probably know that descriptive statistics are central to the world of sports.
 There are many descriptive statistics that we can compute from the data in the table.

 Finally, we compute the estimate's standard error and apply our inferential framework.

 In this section we develop inferential methods for a single proportion that are appropriate when the sample size is too small to apply the normal model to ˆ p.