The test statistic is a number calculated from a statistical test of a hypothesis. It shows how closely your observed data match the distribution expected under the null hypothesis of that statistical test.
The test statistic is used to calculate the p-value of your results, helping to decide whether to reject your null hypothesis.
Levels of measurement, also called scales of measurement, tell you how precisely variables are recorded. In scientific research, a variable is anything that can take on different values across your data set (e.g., height or test scores).
There are 4 levels of measurement:
Nominal: the data can only be categorized
Ordinal: the data can be categorized and ranked
Interval: the data can be categorized, ranked, and evenly spaced
Ratio: the data can be categorized, ranked, evenly spaced, and has a natural zero.
Depending on the level of measurement of the variable, what you can do to analyze your data may be limited. There is a hierarchy in the complexity and precision of the level of measurement, from low (nominal) to high (ratio).
Descriptive statistics summarize and organize characteristics of a data set. A data set is a collection of responses or observations from a sample or entire population.
In quantitative research, after collecting data, the first step of data analysis is to describe characteristics of the responses, such as the average of one variable (e.g., age), or the relation between two variables (e.g., age and creativity).
The next step is inferential statistics, which are tools that help you decide whether your data confirms or refutes your hypothesis and whether it is generalizable to a larger population.
The Akaike information criterion (AIC) is a mathematical method for evaluating how well a model fits the data it was generated from. In statistics, AIC is used to compare different possible models and determine which one is the best fit for the data. AIC is calculated from:
the number of independent variables used to build the model.
the maximum likelihood estimate of the model (how well the model reproduces the data).
The best-fit model according to AIC is the one that explains the greatest amount of variation using the fewest possible independent variables.
ANOVA (Analysis of Variance) is a statistical test used to analyze the difference between the means of more than two groups.
A two-way ANOVA is used to estimate how the mean of a quantitative variable changes according to the levels of two categorical variables. Use a two-way ANOVA when you want to know how two independent variables, in combination, affect a dependent variable.
ANOVA is a statistical test for estimating how a quantitative dependent variable changes according to the levels of one or more categorical independent variables. ANOVA tests whether there is a difference in means of the groups at each level of the independent variable.
The null hypothesis (H0) of the ANOVA is no difference in means, and the alternate hypothesis (Ha) is that the means are different from one another.
In this guide, we will walk you through the process of a one-way ANOVA (one independent variable) and a two-way ANOVA (two independent variables).
Our sample dataset contains observations from an imaginary study of the effects of fertilizer type and planting density on crop yield.
We will also include examples of how to perform and interpret a two-way ANOVA with an interaction term, and an ANOVA with a blocking variable.
Linear regression is a regression model that uses a straight line to describe the relationship between variables. It finds the line of best fit through your data by searching for the value of the regression coefficient(s) that minimizes the total error of the model.