STAT 100 Lecture Notes - Lecture 12: Central Limit Theorem, Sampling Distribution, Statistical Inference

• School: University of Maryland
• Department: Statistics and Probability
• Course: STAT 100
• Professor: Cremins

Document Summary

The mean and standard deviation of the sample mean. Parameter: a number that describes the population, in practice, the value is unknown number, for example, , population mean, , population standard deviation, p, population proportion. Statistic: known value calculated from a sample, a statistic is often used to estimate a parameter, for example, (x-bar), sample mean, s, sample standard deviation, (p-hat), sample proportion. , population standard deviation (p-hat), sample proportion estimate p, population proportion. The process of statistical inference involves using information from a sample to draw conclusions about a wider population. We need to be able to describe the sampling distribution of possible statistic values in order to perform statistical inference. We can think of a statistic as a random variable because it takes numerical values that describe the outcomes of the random sampling process. Sampling terminology (continued: different samples from the same population may yield different values of the sample statistic.