  # Levels in Statistics

Hypothesis Testing > Levels in Statistics

## Levels in Statistics: Contents

1. Levels of independent variables (factors),
2. Confidence Levels,
3. Alpha and Beta levels,
4. Levels of Measurement.

## 1. Levels of Independent Variables (Factors)

A level in factor analysis, or a level of an independent variable, means that the variables can be split up into separate parts. For example, let’s say you were studying the effect of alcohol on performance in a driving simulator. Alcohol — the independent variable — could be composed of different parts: no alcohol, two drinks, four drinks. Each of those parts is called a level.

Combinations of factor levels are called treatments. For example, you might be studying the effect of counseling and medication on depression. You could have many combinations of levels. For example, the treatments could be:

• Treatment 1: No counseling and no medication,
• Treatment 2: Weekly counseling and no medication,
• Treatment 3: No counseling and medication.

## 2. Confidence Levels

A confidence level tells you something about the repeatability of an experiment. For example,a 90% confidence level means that if you repeat the experiment over and over again, the results would match the parameters found in the population 99% of the time. For example, let’s say a poll states it has a 99% confidence level. If that poll were to be repeated a hundred times, 99% of those results would hit the bulls-eye and 1% would be way off the mark.

See: confidence level

## 3. Alpha and Beta Levels

Alpha and beta levels in statistics are used in hypothesis testing. The alpha level (also called the significance level) is the probability of rejecting the null hypothesis when it is true (a “type I error”). A significance level of 5% is the norm. That means you’re willing to accept that you stand a 5% chance of the test telling you there’s something significant when in fact there’s nothing significant going on at all. The opposite is a beta level — the probability that you’ll fail to reject the null hypothesis when it’s false (a “type II error”). If you get a type II error, that means your test fails to detect something significant.

See:

## 4. Levels of Measurement

Levels of measurement (sometimes called scales of measurement) refers to the four types of measuring scales used in statistics: ordinal, interval, ratio, and nominal. For the differences between these levels of measurement, see: measurement scales.

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Statistical concepts explained visually - Includes many concepts such as sample size, hypothesis tests, or logistic regression, explained by Stephanie Glen, founder of StatisticsHowTo.