Correct Option: B. Central Limit Theorem
The Central Limit Theorem (CLT) is essential for understanding the probability distribution of a dataset, especially in scenarios involving large and skewed datasets like customer transaction data. It justifies the use of normal distribution-based methods for inference, even when the underlying data does not follow a normal distribution, by ensuring that the distribution of sample means approximates a normal distribution as the sample size increases. This is crucial for fraud detection as it allows for the application of statistical tests and confidence intervals to identify anomalous transactions.

Why other options are not correct:

• A. Principal component analysis: While useful for dimensionality reduction, it does not directly help in understanding the probability distribution of the data.
• C. Linear regression: This is a predictive modeling technique, not a tool for understanding data distributions.
• D. K-means clustering: This is an unsupervised learning technique for grouping data, not for understanding their probability distributions.