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You are required to carry out large number of observations in experiments because during experiments errors are very common. So to minimize the error, you take a mean (average) of large number of observations so that you can get a more precise value.

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8y ago
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8y ago

A single measure is subject to measurement error or even coming across a rare event on your first trial. It is important to carry out a large number of observations in an experiment because, according to the Central Limit Theorem, whatever the underlying distribution of the variable being measured, the mean of a number of observations will approach the Normal distribution with the mean which is the true value of the variable and a standard deviation (a measure of spread) which reduces with the [square root of the] number of observations. So, not only do you get a more accurate estimate of the true value but you are then also able to use statistical tests on the variable using a wide range of properties of the Normal distribution. For example, you can derive a 95% confidence interval (or any other percentage) - a range of values such that you can be 95% sure the true value of the variable is in.

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Q: Why is it important to carry out a large number of observations in experiments?
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