Statistics - Statistical Inference
Using data analysis and statistics to make conclusions about a population is called statistical inference.
The main types of statistical inference are:
- Hypothesis testing
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."
Hypothesis testing is a method to check if a claim about a population is true. More precisely, it checks how likely it is that a hypothesis is true is based on the sample data.
There are different types of hypothesis testing.
The steps of the test depends on:
- Type of data (categorical or numerical)
- If you are looking at:
- A single group
- Comparing one group to another
- Comparing the same group before and after a change
Some examples of claims or questions that can be checked with hypothesis testing:
- 90% of Australians are left handed
- Is the average weight of dogs more than 40kg?
- Do doctors make more money than lawyers?
Statistical inference methods rely on probability calculation and probability distributions.
You will learn about the most important probability distributions in the next pages.