Statistical significance is a term used by researchers to state that it is unlikely their observations could have occurred under the null hypothesis of a statistical test. Significance is usually denoted by a p -value, or probability value.
1 tree). Categorical variables represent groupings of things (e.g. the different tree species in a forest). Types of categorical variables include: Ordinal: represent data with an order (e.g. rankings).
Non-parametric tests don’t make as many assumptions about the data , and are useful when one or more of the common statistical assumptions are violated. However, the inferences they make aren’t as strong as with parametric tests.
They can be used to: determine whether a predictor variable has a statistically significant relationship with an outcome variable. estimate the difference between two or more groups. Statistical tests assume a null hypothesis of no relationship or no difference between groups.
Parametric tests usually have stricter requirements than nonparametric tests, and are able to make stronger inferences from the data. They can only be conducted with data that adheres to the common assumptions of statistical tests.
Independence of observations (a.k.a. no autocorrelation): The observations/variables you include in your test are not related (for example, multiple measurements of a single test subject are not independent, while measurements of multiple different test subjects are independent).
Types of variables. The types of variables you have usually determine what type of statistical test you can use. Quantitative variables represent amounts of things (e.g. the number of trees in a forest). Types of quantitative variables include: