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Using data analysis and statistics to make conclusions about a population is called statistical inference
Using data analysis and statistics to make conclusions about a population is called statistical inference.
The main types of statistical inference are:
Statistics from a sample are used to estimate population parameters.
The most likely value is called a point estimate.
There is always uncertainty when estimating.
The uncertainty is often expressed as confidence intervals defined by a likely lowest and highest value for the parameter.
An example could be a confidence interval for the number of bicycles a Dutch person owns:
"The average number of bikes a Dutch person owns is between 3.5 and 6."
There are different types of hypothesis testing.
The steps of the test depends on:
Some examples of claims or questions that can be checked with hypothesis testing:
Statistical inference methods rely on probability calculation and probability distributions.
You will learn about the most important probability distributions in the next pages.