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 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.

Conditional independence in probability theory refers to a situation where two events are independent given the occurrence of a third event. Mathematically, two events A and B are conditionally independent given a third event C if the probability of the intersection of A and B given C is the product of the probabilities of A given C and B given C.

The likelihood in Bayes' theorem represents the probability of the evidence given the event. It quantifies the extent to which the evidence supports the event.