These types of statistics are used to make valid generalizations or inferences about population parameters.

This determines which summary statistics, graphs, and analysis are possible and sensible.

The first step in this process is having a hypothesis about a population of interest.

The parametric statistic that allows you to look at the difference between group means.

Also known as categorical or qualitative, this level of measurement is the most basic and includes descriptions or labels. Sex is an example.

The statistics that help us understand the characteristics of the sample we collected. For example, the mean age of your sample.

These statistics makes no assumptions that the data or population have any characteristic structure.

In ordinal data, although there is a meaningful order to the data, THIS may not be equal. For example, first versus second versus third place in a race.

The generation of p-values and confidence intervals are generally necessary in this process.

A non-parametric statistical approach is advised when this has been violated.

The most precise level of measurement, interval/ratio, also known as scale, quantitative, and parametric, can be used to calculate which common basic statistics?

Statistics, research design, other research results, and clinical knowledge must all be considered when doing this.

Everything we do in statistics is this.

This is the type of graphical representation that should be used to display the results of a satisfaction survey, which include a Likert scale from very unsatisfied to very satisfied.

Observation, pattern, question, hypothesis, experiment and conclusion are the six steps involved in this process.