Random Sampling

If we want to run a study or an experiment, we need to be very careful about the way we sample our data. Why? Because it affects the accuracy of our results. One method to ensure accuracy is random sampling. But how effective is this strategy, and how does it work?

What is a sample?

When we take a "sample," we take a collection of data from only part of the whole. This "whole" is called the population.
For example, let''s say we have a lake with 1,000 fish in it of several different kinds, and we collect 50 of them at random. The original 1,000 is the population and the 50 we collected is the sample.

Random sampling, explained

Random sampling is when you collect data from a population in a completely random fashion. Here are some examples:

• Rolling a die
• Putting names into a hat and choosing them blindly
• Drawing straw blindly
• Using a computerized random number generator

Random sampling is a method of selecting a sample from a population in a way that each individual in the population has an equal chance of being selected, and it is important to ensure that the sample is representative of the population to obtain accurate and meaningful results.

Topics related to the Random Sampling

Range (of Data)

Developing a Probability Distribution from Empirical Data

Line of best fit

Flashcards covering the Random Sampling

Statistics Flashcards

Common Core: High School - Statistics and Probability Flashcards

Practice tests covering the Random Sampling

Probability Theory Practice Tests

Common Core: High School - Statistics and Probability Diagnostic Tests

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