In probability theory, an experiment refers to a process or procedure that produces outcomes. The outcomes depend on chance or randomness. For example, tossing a coin or rolling a die is considered a random experiment because the outcome is not certain but depends on chance.

The Central Limit Theorem allows us to make inferences about the Population based on sample data. It states that, with a large enough sample size, the sample mean will be normally distributed around the population mean. This enables us to estimate the parameters of the population and make predictions based on the sample data.

In a t-test or Z-test, the p-value represents the probability of obtaining a sample statistic as extreme or more extreme than the observed statistic, assuming the null hypothesis is true.

A Type I error occurs when we reject the null hypothesis, even though it is true. This is also known as a "false positive" error.

The two main types of data in statistics are Qualitative and Quantitative. Qualitative data, also known as categorical data, represents characteristics or attributes and cannot be mathematically quantified. Quantitative data, on the other hand, is numerical, representing measurements or counts that can be quantified mathematically.

Mutually exclusive events are events that cannot occur at the same time - the occurrence of one event excludes the occurrence of the other(s). On the other hand, independent events are those where the occurrence of one event does not affect the probability of the occurrence of the other event(s). The concepts are related but distinct.

In Bayes' Theorem, the posterior probability is the updated probability of an event after new evidence has been observed. It is calculated by multiplying the likelihood and the prior probability and then dividing by the probability of the evidence.

The range of values around the point estimate that captures the true population parameter at some predetermined confidence level is called a confidence interval. Confidence intervals are used in statistics to indicate the reliability of an estimate.

To prevent overfitting, we can apply a technique called regularization in polynomial regression. Regularization involves adding a penalty term to the loss function during the process of training a model. This penalty term discourages the coefficients of the model from reaching large values, leading to a simpler model that's less likely to overfit.

The primary purpose of an ANOVA (Analysis of Variance) test is to compare the means of three or more groups to see if they are different. It is a way to find out if survey or experiment results are significant. In other words, they help to figure out if you need to reject the null hypothesis or accept the alternate hypothesis.