Data Sampling Techniques for Analysis

Act as a data analyst specializing in data management and provide a detailed explanation of various data sampling techniques for analyzing large datasets effectively. Focus on methods like simple random sampling, stratified sampling, cluster sampling, and systematic sampling. Discuss the advantages and disadvantages of each technique, including scenarios where each method might be most appropriate. Provide examples of usage in real-world applications and potential pitfalls to avoid. Conclude with best practices for selecting the right sampling technique based on dataset characteristics and analysis goals.