A higher significance level (α) increases the likelihood of rejecting the null hypothesis. This is because you're setting a higher bar for the amount of evidence needed to retain the null hypothesis.

If a distribution is flatter than a normal distribution, it is said to have negative kurtosis. This type of distribution has lighter tails and a flatter peak than the normal distribution. It is also called platykurtic.

A sample in statistics is a subset of individuals or observations from a larger population. Sampling is a key concept in statistics and data science because it allows us to collect and analyze a manageable amount of data that represents a larger group.

Box plots are particularly useful for identifying outliers in a dataset. The box plot displays a summary of the data distribution including minimum, first quartile, median, third quartile, and maximum. Outliers are typically represented as individual points that are far from the 'box' and 'whiskers'.

The Central Limit Theorem (CLT) states that, given certain conditions, the mean of a sufficiently large number of independent random variables will be approximately normally distributed, regardless of the shape of the original distribution. This underpins many statistical methods, including hypothesis tests and confidence intervals, which may assume normality of the sampling distribution.

PCA reduces the dimensionality of a dataset by creating new uncorrelated variables that successively maximize variance. These new variables or "principal components" can replace the original variables, thus reducing the data's dimensionality.

In a 95% confidence interval, if the true population parameter lies outside of the interval, it is considered a Type I error. This is when the null hypothesis is true, but is incorrectly rejected.

Simple random sampling is a basic type of sampling method where each individual in the population has an equal chance of being selected. This ensures that the sample will be representative of the population, making it easier to make accurate inferences about the whole population.

The eigenvalue of a component in PCA is directly proportional to the variance of that component. In other words, a larger eigenvalue corresponds to a larger amount of variance explained by that principal component.

Bayes' theorem is a way of finding a probability when we know certain other probabilities. The probabilities that we know are usually conditional probabilities, and Bayes' theorem is used to 'reverse' these probabilities.

The interaction effect in a two-way ANOVA reveals whether the effect of one independent variable depends on the level of the other independent variable. This allows us to understand how the independent variables relate to each other.

The Wilcoxon Signed Rank Test is a non-parametric test that uses ranks and is used for ordinal data, while the paired t-test is a parametric test that uses actual values and is typically used for continuous data.