News / Blog: #statistical

What is the T-Test?

The T-test is a statistical test used to determine whether the means of two groups are significantly different from each other. It is usually used to test whether a particular treatment or experiment has a significant effect on a group of people or objects. The T-test is based on the assumption that the values in both groups are normally distributed.

What is standardization (statistics)?

Standardization is a statistical process in which a given set of numbers is converted into a new set of numbers that all have the same mean and standard deviation. The newly generated set of numbers is called standardized data. It is often used to compare different sets of data or to put data into a more homogeneous format.

What is a nonparametric test?

A nonparametric test is a statistical test that does not require any assumptions about the distribution of the data. They are particularly useful when you have data that is not normally distributed or when you do not have information about the distribution available. Nonparametric tests tend to be less powerful than parametric tests, but in many cases they can be used to test the same hypotheses.

What is the chi-square test?

The chi-square test (also chi-square test or chi-square test) is a statistical test used to test hypotheses about the independence of two characteristics. It is often used to test the significance of an observational or experimental data analysis. The chi-square test is used to find out whether a particular set of observations or measurements is significantly different from the expected value. The test is based on comparing the empirical probabilities of a particular outcome with the theoretical probability of the same outcome.

What is multicollinearity?

Multicollinearity is a statistical phenomenon in which there is variable correlation between multiple variables in a model. It occurs when two or more variables are highly correlated with each other, which can lead to bias in estimates. Multicollinearity can affect the precision of estimates and make it more difficult to determine the individual effects of a variable on the model outcome.