When we say a non-parametric test makes fewer assumptions about the data distribution, we mean that the data does not have to follow a specific distribution, such as the normal distribution. Non-parametric tests are distribution-free tests and make no assumption about the probability distribution of the variables.

Pearson's Correlation Coefficient measures the linear correlation between two variables. It quantifies the degree to which two variables are related to each other.

The null hypothesis in a one-way ANOVA test is that all group means are equal. This hypothesis is tested against the alternative that at least one group mean is different.

The harmonic mean is useful in the analysis of rates as it gives more weight to smaller values. This can be particularly useful when dealing with rates or ratios, for example, in calculating average speed. The harmonic mean is the reciprocal of the arithmetic mean of the reciprocals, making it a robust measure for rates.

Techniques like forward selection, backward elimination, and stepwise regression are commonly used for model selection in multiple regression.

The presence of a pattern in the residuals of a multiple linear regression model can indicate a violation of the independence assumption. This assumption requires that the residuals, which are the differences between the observed and predicted values of the dependent variable, are independent of each other. If a pattern is observed in the residuals, it may indicate that the residuals are not independent, and the model may not provide valid results.

The Sign Test ignores the magnitude of the differences between paired observations, and only considers the sign of the differences.

Larger sample sizes reduce the standard error and thus, the width of the confidence interval becomes narrower. This means that with larger samples, our estimates are more precise.

The Kaiser-Meyer-Olkin (KMO) measure is a measure of how suitable the data is for factor analysis. It determines the adequacy for each observed variable and for the complete model. KMO estimates vary between 0 and 1. A value of 0 indicates that the sum of partial correlations is large relative to the sum correlations, implying diffusion in the pattern of correlations (hence, factor analysis will be likely inappropriate).

Quantitative data can be further classified as discrete and continuous. Discrete data is countable and has a finite number of possible values, such as the number of students in a class. Continuous data can take any value within a given range, such as the weight of a person.