Sampling Techniques Video Lecture | Crash Course for GATE Data Science & Artificial Intelligence - GATE Data Science and Artificial Intelligence (DA)
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FAQs on Sampling Techniques Video Lecture - Crash Course for GATE Data Science & Artificial Intelligence - GATE Data Science and Artificial Intelligence (DA)
| 1. What are the different types of sampling techniques used in data science? |  |
| 2. Why is sampling important in data science and artificial intelligence? | |
Ans. Sampling is crucial in data science and AI because it allows researchers and practitioners to make inferences about a larger population without needing to analyze every individual. It saves time and resources, reduces data processing costs, and can lead to quicker insights. Proper sampling ensures that the sample accurately represents the population, which is essential for the validity of statistical analyses and the development of machine learning models.
| 3. How do you determine the sample size needed for a study? | |
Ans. Determining the sample size depends on various factors, including the desired confidence level, margin of error, population size, and the expected variability within the population. Statistical formulas, such as the Cochran formula, can help calculate the necessary sample size. Additionally, pilot studies can provide estimates of variability, and software tools can assist in making these calculations more straightforward.
| 4. What is the difference between probability and non-probability sampling? | |
Ans. Probability sampling involves methods where each individual in the population has a known chance of being selected, ensuring that the sample is representative of the population. Non-probability sampling, on the other hand, does not guarantee that every individual has a chance of being included, which can introduce bias. Examples of probability sampling include random and stratified sampling, while convenience and quota sampling are examples of non-probability methods.
| 5. How can bias affect sampling results in data analysis? | |
Ans. Bias in sampling can lead to skewed results, misinterpretations, and incorrect conclusions about the population. If certain groups are overrepresented or underrepresented due to the sampling technique used, the findings may not accurately reflect the true characteristics of the entire population. This can compromise the validity of the analysis and undermine the reliability of any predictive models developed from the biased sample.
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Mar 31, 2025
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