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: 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. That’s because you can’t know the true value of the population parameter without collecting data from the full population. However, with random sampling and a suitable sample size, you can reasonably expect your confidence interval to contain the parameter a certain percentage of the time. Example: Point estimate and confidence interval
You want to know the average number of paid vacation days that employees at an international company receive. After collecting survey responses from a random sample, you calculate a point estimate and a confidence interval.