Title: Inferential Statistics - Overview, Parameters, Testing Methods
Headings: Inferential Statistics
Inferential Statistics
What is Inferential Statistics?
Summary
Population Parameters, Sample Statistics, Sampling Errors, and Confidence Intervals
Hypothesis Testing
Other Testing Methods
More Resources

Content: A point estimate is one estimate of a parameter (e.g., sample mean). Seeing as a sample is merely a portion of a larger population, sample data does not capture information on the whole population, and this results in a sampling error. Sampling error can be defined as the difference between respective statistics (sample values) and parameters (population values). The sampling error is inevitable when sample data is being used; therefore, inferential statistics can be ambiguous. To minimize the uncertainty created by the presence of sampling errors, probability sampling methods can be applied in data analysis. Confidence intervals allow for interval estimations for population values (or parameters) by utilizing statistical variabilities. Confidence intervals account for sampling errors
Sampling Errors Sampling errors are statistical errors that arise when a sample does not represent the whole population. They are the difference between the
. Confidence intervals, as with interval estimates, provide a range of values in which a parameter is likely to be found, and therefore, show the likelihood of point estimate uncertainty.