Notes on Data Types, Variables, and Sampling from Video Transcript (copy)

Population

The entire group of interest in a study.

Sample

A subset drawn from the population to learn about the population.

Distribution

Concerns the frequency of each value in a dataset.

Central tendency

The measure of the averages or typical values in a dataset.

Variability

Concerns how spread out the values are in a dataset.

Quantitative variables

Numerical variables that can be measured in units, e.g., weight or temperature.

Qualitative variables

Categorical variables that represent categories or types rather than numerical values.

Discrete variables

Countable variables that typically take whole number values.

Continuous variables

Variables that can take any value within a range and do not have gaps.

Population mean

The average of a population, calculated as $\mu = \frac{1}{N} \sum<em>{i=1}^{N} x</em>i$.

Sample mean

The average of a sample, calculated as $\bar{x} = \frac{1}{n} \sum<em>{i=1}^{n} x</em>i$.

Population proportion

The proportion of individuals in a population with a certain characteristic, calculated as $P = \frac{\text{number with characteristic}}{N}$.

Sample proportion

An estimate of proportion from a sample, calculated as $\hat{p} = \frac{\text{number with characteristic in sample}}{n}$.

Stratified random sampling

A sampling method that divides the population into subgroups and randomly samples from each to ensure representation.

Systematic sampling

A method of sampling where every nth member of the population is selected after a random starting point.