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).

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.

A binomial distribution is used when there are exactly two mutually exclusive outcomes of a trial (often referred to as a success or a failure). It models the total number of successes in a fixed number of independent trials.

The key assumption for conducting a Kruskal-Wallis Test is that the samples must be independent.

Standard error is a measure of the statistical accuracy of an estimate, equal to the standard deviation of the theoretical distribution of a large population of such estimates.

Converting Qualitative data into quantitative data involves the process of coding. This process involves assigning numerical values to qualitative information (such as categories or themes) so that they can be manipulated and analyzed statistically. For example, if you have data on types of pets (dogs, cats, etc.), you can assign a numerical code (1 for dogs, 2 for cats, etc.) to transform this qualitative data into quantitative 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.

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.

A sample might not accurately represent the population when the sampling method is biased. In this case, the sample may not be diverse enough or inclusive of all relevant aspects of the population. This can lead to skewed results and inaccurate inferences about the population. Hence, it's essential to choose an unbiased sampling method.

In a uniform distribution, all outcomes are equally likely. This distribution is characterized by two parameters, a and b, which are the minimum and maximum values, respectively. The probability of any outcome is constant and equal across the entire range of the distribution.