Sample "Sampling Bias": When using samples, sampling bias is perhaps the most significant concern. If the sample is not truly representative of the population, the findings may not accurately reflect its characteristics. Researchers must employ strategies to minimize bias and ensure the sample's representativeness. 2. What is the difference between a'smart' and a'smart'? Sample Size: Sample size is another critical consideration. A small sample may not adequately capture the diversity present within the population, leading to limited generalizability of findings. Determining an appropriate sample size requires careful consideration of statistical power and the desired level of precision. 3. What is the difference between a'smart' and a'smart'? Randomness: Random sampling is an effective method for minimizing bias and enhancing representativeness. However, achieving true randomness in sample selection is not always feasible, particularly in practical research settings. Researchers must balance the ideal of random sampling with the constraints of real-world research conditions. Example from Computer Science: Question: How efficient is a new sorting algorithm compared to existing ones?Target Population: All computer science students at a specific university.Sampling