A series of free Statistics Lectures with lessons, examples & solutions in videos.
This is the fifteenth page of the series of free video lessons, “Statistics Lectures”. These lectures discuss parameters, statistics, sampling error, distribution of the sample mean and introduce the Central Limit Theorem.
13: Scatter Plots & Pearson’s r Correlation
14: Linear Regression & Spearman Correlation
16: Sample Proportions & Confidence Intervals 1
17: t-Distribution & Confidence Intervals 2
Jump to Table of Contents
A characteristics that describes a population is called a parameter. Because it is often difficult
(or impossible) to measure an entire population, parameters are most often estimated.
A characteristic that describes a sample is called a statistic.
Statistics are most often used to measure the value of unknown parameters.
Sampling error is any difference that exists between a statistic and its corresponding parameter.
The Distribution of the Sample Mean is a probability distribution of all possible values of a sample mean, computed from a sample size n. The standard deviation of the sampling distribution is also known as the Standard Error of the Mean.
The Central Limit Theorem states that regardless of the shape of the population distribution, the distribution of the sample means will be approximately normal. The distribution of the sample means will become more normal as its sample size increases.
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