A histogram with two distinct peaks is referred to as a bimodal distribution. This might suggest that the data contains two different groups, each with their own mode, or most common value.

The prior probability in Bayes' Theorem is the initial or original probability of an event before new evidence is taken into account. It represents our initial belief about the event.

The Chi-square test for goodness of fit is used to determine whether the observed distribution of data follows an expected distribution.

PCA can be implemented using SVD. Both techniques can be used for dimensionality reduction, and they both rely on eigenvalue decomposition, but SVD decomposes the data matrix directly, while PCA works on the covariance matrix of the data.

A wider confidence interval suggests a higher level of uncertainty about the estimate because the range of values for the population parameter is larger.

In multiple linear regression, each coefficient represents the average change in the dependent variable for one unit of change in the independent variable, while holding all other independent variables constant.

Bartlett's Test of Sphericity is used in factor analysis to test the hypothesis that the variables are uncorrelated in the population. In other words, the population correlation matrix is an identity matrix. A significant test indicates that a factor analysis may be useful with your data.

A scatter plot is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data. It's used to observe and show the relationship between two numeric variables.

The probability of a Type I error (false positive) is the same as the significance level of a test. A significance level of 0.05, for instance, means there's a 5% chance of rejecting a true null hypothesis (Type I error).

The standard error is a statistical term that measures the standard deviation of the sampling distribution. In other words, it provides a measure of the variability or dispersion of sample means around the population mean. A smaller standard error indicates that the sample mean is likely to be a closer approximation to the population mean.

If the assumptions of a parametric test are violated, it might be appropriate to use a non-parametric statistical method. Non-parametric methods have fewer assumptions and can be used with different types of data.

The key difference between parametric and non-parametric statistical methods is the assumptions they make about the data distribution. Parametric methods assume that the data follow a certain distribution, while non-parametric methods do not make these assumptions.