A histogram is a graphical representation of the distribution of a dataset. It is an estimate of the probability distribution of a continuous variable. To construct a histogram, the first step is to "bin" the range of values, i.e., divide the entire range of values into a series of intervals, and then count how many values fall into each interval.

The primary assumption for a Z-test is that the population standard deviation is known. While a large sample size and normal distribution are often associated with Z-tests, they're not the primary assumption.

A negative kurtosis value indicates that the distribution has lighter tails and a flatter peak than the normal distribution. It means there are fewer outliers (extreme values), thus it is less outlier-prone than a normal distribution.

In an ANOVA test, the F-statistic is the ratio of the between-group variability to the within-group variability. In other words, it measures how much the means of each group vary between the groups, compared to how much they vary within each group. A larger F-statistic implies a greater degree of difference between the group means.

Homoscedasticity refers to the assumption that the residuals (errors) have constant variance at each level of the independent variable(s). This is important for the reliability of the regression model.

Stratified random sampling differs from simple random sampling in that it first divides the population into non-overlapping groups, or strata, based on specific characteristics, and then selects a simple random sample from each stratum. This can ensure that each subgroup is adequately represented in the sample, which can increase the precision of estimates.

Bar plots are commonly used in data analysis to compare the frequency, count, or proportion of categorical variables. Each category is represented by a separate bar, and the length or height of the bar represents its corresponding value.

Conditional independence of A and B given C means that knowing that C has occurred does not change the relationship between A and B. In other words, the occurrence of event C does not affect the independence of events A and B.

When computing the Pearson correlation coefficient, it is assumed that there is a linear relationship between the variables. Furthermore, it's also assumed that the variables are continuous and that the data is homoscedastic (i.e., the variance of the errors is the same across all levels of the variables).

The variance is the square of the standard deviation. Standard deviation is a measure of dispersion in a dataset and variance is a square of it, meaning that they both represent the same concept of dispersion, but in different units.