Title: Inferential Statistics | An Easy Introduction & Examples
Headings: An introduction to inferential statistics
An introduction to inferential statistics
Table of contents
Descriptive versus inferential statistics
Descriptive statistics
Inferential statistics
Sampling error in inferential statistics
Estimating population parameters from sample statistics
Confidence intervals
What is your plagiarism score?
Hypothesis testing
Comparison tests
Correlation tests
Regression tests
Frequently asked questions about inferential statistics
What’s the difference between descriptive and inferential statistics?
What’s the difference between a statistic and a parameter?
What is sampling error?
What is hypothesis testing?
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Pritha Bhandari

Content: There are two important types of estimates you can make about the population: point estimates and interval estimates. A point estimate is a single value estimate of a parameter. For instance, a sample mean is a point estimate of a population mean. An interval estimate gives you a range of values where the parameter is expected to lie. A confidence interval is the most common type of interval estimate. Both types of estimates are important for gathering a clear idea of where a parameter is likely to lie. Confidence intervals
A confidence interval uses the variability around a statistic to come up with an interval estimate for a parameter. Confidence intervals are useful for estimating parameters because they take sampling error into account. While a point estimate gives you a precise value for the parameter you are interested in, a confidence interval tells you the uncertainty of the point estimate.