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: 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. They are best used in combination with each other. Each confidence interval is associated with a confidence level. A confidence level tells you the probability (in percentage) of the interval containing the parameter estimate if you repeat the study again. A 95% confidence interval means that if you repeat your study with a new sample in exactly the same way 100 times, you can expect your estimate to lie within the specified range of values 95 times. Although you can say that your estimate will lie within the interval a certain percentage of the time, you cannot say for sure that the actual population parameter will.