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.

The standard error is a statistical term that measures the standard deviation of the sampling distribution. In other words, it provides a measure of the variability or dispersion of sample means around the population mean. A smaller standard error indicates that the sample mean is likely to be a closer approximation to the population mean.

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 confidence interval is calculated using the sample mean plus and minus the standard error. Specifically, it is calculated by taking the point estimate and adding/subtracting the margin of error (which is the standard error multiplied by the relevant Z-value or T-value).

With a large sample size, small differences may become statistically significant, which can lead to overstating the evidence against the null hypothesis. In contrast, with a small sample size, we might not have enough power to detect an effect, even if one exists.