A statistics level is the value of input in an experimental run.
The levels of independent variables (Factors).
A level in factor analysis or an independent variable level is a way to break down the variables into smaller parts. Let’s take, for example, the effects of alcohol on driving performance in a simulator. The independent variable, alcohol — can be made up of several parts: none, two, or four drinks. Each part is called a Level.
Combinations of factors are known as treatments. You might, for example, be looking at the effects of medication and counseling on depression. There could be many levels. You could have:
- Treatment 1 : There is no counseling or medication.
- 2: Weekly counseling, no medication
- Treatment 3 :No counseling or medication
Confidence in statistics level indicates the reproducibility of an experiment. A 90% confidence level would mean that the results of an experiment could be repeated over and over until they match the parameters found within the population 99 percent of the time. Let’s take, for example, a poll that claims it has a confidence level of 99%. This poll could be repeated 100 times. 99% would be correct, and 1% would be wildly off.
Alpha and Beta statistics levels
Hypothesis testing uses the beta and alpha levels of statistics. The alpha, also known as the significance, is the probability that will reject the null hypothesis if it is true (an “error”). A significance level of 5% would be the norm. This means that you are willing to accept that there is a 5% chance that the test will tell you something is significant, even though there is nothing. A beta level is a probability that the test will fail to reject the null hypothesis if it’s false. This is called a ” type 2 error”. A type II error means that your test failed to detect anything significant.
Different levels of measurement
Levels of measurement, also known as scales of measurement, refer to the four types of measuring scales used for statistics: ordinal (interval, ratio, and nominal).