Why is the random sampling assumption important in statistical inference? Suppose that you had to select...

Why is the random sampling assumption important in statistical inference? Suppose that you had to select a random sample of 100 items from a production line. How would you propose to do this?

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The random sampling assumption important in statistical inference for to get UNBIASED RESULTS/RESULT. A sample is random when each data point in your population has an equal chance of being included in the sample; therefore selection of any individual happens by chance, rather than by choice. This reduces the chance that differences in materials or conditions strongly bias results.

If I need to select a random sample of 100 items from a production line, : To draw a simple random sample of 100 from a production line, each entry would need to be numbered sequentially. If there were 10,000 items, each would be numbered and if the sample size were 100 then 100 numbers between 1 and 10,000 would need to be randomly generated by a computer. Each number will have the same chance of being generated by the computer (in order to fill the simple random sampling requirement of an equal chance for every unit).

Also we can use roulette table to draw those 100 items, but before doing doing so we must keep one thing in my that item must be random.

orchestra answered 1 year ago

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