Sampling Distribution: Introduction
Introduction
The main idea here is to discuss sampling distribution of the mean and the sampling error.
We will introduce the sampling distributions, describe the sampling distribution of the mean for normal populations
using the Central Limit Theorem. We will also discuss how to calculate probabilities using sampling distributions.
Why Sample the Population?
The reason why we sample the population is because most of the times it's impossible to consider
every single item in the population due to both cost and time. For example, engaging the entire population is not
something that students or professionals would like to do.
It is also important to note that the sample results are adequate for decision making and future forecasting.
The sample results are usually adequate.
Parameter versus statistic
- Population consists of the whole group of individuals that we would like to get in touch with
but usually difficult to do it directly.
- A parameter is a number that describes a characteristic of the population. Parameters are usually unknown
- Sample represents part of the population for which the data is available.
- A statistic is a number describing a characteristic of a sample, which is usually used to estimate an unknown population parameter.
Remember the below illustration which was introduced in the Introduction to Statistics Section:
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Date of last modification: March 25, 2019