The K-means clustering method can have several issues: it doesn't work well with non-numeric data, it's sensitive to outliers (since outliers can significantly move the cluster centroids), and it has difficulty handling clusters that are non-spherical or have varying sizes and densities.

A positive slope in a scatter plot suggests that the two variables are positively correlated. This means as one variable increases, the other variable also tends to increase.

PCA typically reduces the interpretability of the original features. This is because each principal component is a linear combination of all the original features, making it difficult to understand how individual features affect the outcome.

Bayes' Theorem is primarily used to update prior beliefs given new data. It's a way to go from a prior probability to a posterior probability, which is a more accurate estimate because it incorporates new evidence.

The power of a test is the probability that it correctly rejects a false null hypothesis (true positive). It's the complement of a Type II error. As Type I error probability increases, power also increases because we're more willing to reject the null hypothesis. However, a Type II error decreases power because it's a missed opportunity to reject a false null hypothesis.

Outliers can greatly affect the correlation coefficient, making it misleading. If outliers are in the same direction, they can inflate the correlation. If they are in opposite directions, they can deflate or even reverse the sign of the correlation. Hence, it's important to handle outliers before conducting correlation analysis.

Larger standard error leads to a wider confidence interval. The standard error measures the variability in the sampling distribution and a larger standard error suggests more variability, which in turn leads to less precise estimates and wider intervals.

The interquartile mean focuses on the data between the first quartile (25th percentile) and the third quartile (75th percentile), excluding the lowest 25% and the highest 25% of data points. This makes it less influenced by outliers and extreme values, hence a more robust measure of central tendency for skewed or asymmetrical distributions.

A Type I error, also known as a false positive, occurs when we reject a true null hypothesis. This means we've found evidence of an effect or difference when there really isn't one.

The Addition Rule of Probability is used when we want to find the probability that either of two events happens. This rule states that the probability of either of two mutually exclusive events occurring is the sum of their individual probabilities.