Bubble Sort is often used for sorting smaller lists due to its simplicity and ease of understanding. However, it may not be the most efficient for larger datasets. Quick Sort, Merge Sort, and Binary Search are more suitable for larger datasets and offer better performance.

The primary goal of time series analysis is to identify patterns and trends over time, helping analysts understand the underlying factors influencing the data and make predictions for future events based on historical observations.

Conversion Rate is a critical metric to prioritize when aiming to improve online customer engagement. It measures the percentage of users who take a desired action, such as making a purchase or signing up. A higher conversion rate indicates better engagement and effectiveness of the online platform. Other metrics like CTR, Bounce Rate, and CLV provide valuable insights but may not directly reflect engagement effectiveness.

Apache Spark is a common tool used for ETL processes in data warehousing. It provides a fast and general-purpose cluster computing system for big data processing and analytics.

Anomaly Detection is well-suited for detecting unusual patterns, making it effective in identifying fraudulent activities in a telecommunications setting. Clustering, Classification, and Regression have different purposes and may not be as effective for fraud detection.

Percentiles divide the data set into 100 equal parts, while quartiles divide it into four parts. Percentiles are more granular, providing a more detailed view of data distribution.

In regression analysis, the correlation coefficient measures the strength and direction of a linear relationship between two variables. It ranges from -1 to 1, where 1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship.

To select all columns from a table named 'Customers' in SQL, you use the syntax: SELECT * FROM Customers. The asterisk (*) is a wildcard character that represents all columns.

The P-value is used in hypothesis testing to assess the evidence against a null hypothesis. A small P-value suggests that the null hypothesis is unlikely, leading to the rejection of the null hypothesis in favor of the alternative hypothesis.

Data consistency is ensured by ACID (Atomicity, Consistency, Isolation, Durability) compliance. ACID compliance guarantees that database transactions are processed reliably and consistently, which is crucial in scenarios where data consistency is a top priority.