Population and Sample Video Lecture | Crash Course for GATE Data Science & Artificial Intelligence - GATE Data Science and Artificial Intelligence (DA)
FAQs on Population and Sample Video Lecture - Crash Course for GATE Data Science & Artificial Intelligence - GATE Data Science and Artificial Intelligence (DA)
| 1. What is the difference between a population and a sample in data science? |  |
Ans. In data science, a population refers to the entire set of individuals or items that are the subject of a study, while a sample is a subset of that population selected for analysis. The key difference is that a population includes all possible members, whereas a sample is a smaller group meant to represent the population for statistical inference.
| 2. Why is it important to use sampling methods in data analysis? | |
Ans. Sampling methods are important because they allow researchers to make inferences about a larger population without the need to collect data from every individual. This saves time and resources, reduces costs, and can still yield accurate insights if the sample is chosen properly through techniques like random sampling or stratified sampling.
| 3. What are some common sampling techniques used in data science? | |
Ans. Common sampling techniques include simple random sampling, stratified sampling, systematic sampling, and cluster sampling. Each method has its own strengths and weaknesses, and the choice depends on the research goals, the nature of the population, and available resources.
| 4. How do you determine the appropriate sample size for a study? | |
Ans. The appropriate sample size can be determined using several factors, including the population size, the desired level of confidence, the margin of error, and the variability within the population. Statistical formulas and power analysis are often used to calculate the ideal sample size to ensure the results are statistically significant.
| 5. What is the role of sampling bias in data analysis? | |
Ans. Sampling bias occurs when certain members of the population have a higher or lower chance of being selected in the sample, leading to skewed results. It can undermine the validity of a study’s conclusions and lead to incorrect generalizations about the population. Addressing sampling bias is critical to ensure the reliability of data analysis results.
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