Random Sampling

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What is Random Sampling?

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Random sampling is a technique used to select a subset of individuals from a larger population, where each individual has an equal chance of being chosen.

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What is a Biased Sample?

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A biased sample is a sample that does not accurately represent the population from which it is drawn, leading to skewed results.

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What is an Unbiased Sample?

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An unbiased sample is a sample that accurately represents the population, allowing for valid conclusions to be drawn.

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Why is Random Sampling important?

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Random sampling is important because it helps ensure that the sample reflects the diversity of the population, reducing bias and increasing the reliability of results.

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Example of a Biased Sample:

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Surveying only the students in a specific club to determine the favorite sport of all students.

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Example of an Unbiased Sample:

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Selecting students randomly from all grades to find out how much time they spend on homework.

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What is Sampling Error?

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Sampling error is the difference between the results obtained from a sample and the actual results from the entire population.

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How can you reduce Bias in Sampling?

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To reduce bias, use random selection methods, ensure diversity in the sample, and avoid self-selection.

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What is Stratified Sampling?

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Stratified sampling is a method where the population is divided into subgroups (strata) and random samples are taken from each stratum.

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What is Systematic Sampling?

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Systematic sampling is a method where you select every nth individual from a list of the population.