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To put it in the simplest possible terms, if errors happen in a small sample size they create a larger discrepancy than in a larger sample size.

Let's say you're trying to find average height, for instance, and use 3 people. One is 180cm, one is 190... and the third is 230. Now you've got an average of 2m, which is obviously not true. Let's say you add 2 more people to your sample, both of normal height. (180 and 185) This makes for an average of 193cm, a bit closer to normal. As you get into larger sample sizes this discrepancy from the reading of 230, regardless of whether this is an accurate reading or an error, will get smaller.

Also, if you are speaking of some type of electronic or scientific instrumentation, large sample sizes help average out:

1. Electrical noise

2. Variations in measurements due to heat/hysteresis/accuracy of the equipment.

3. Also, don't forget human error in setup, recording, and even variations from one same to the next.

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To put it in the simplest possible terms, if errors happen in a small sample size they create a larger discrepancy than in a larger sample size. Let's say you're trying to find average height, for instance, and use 3 people. One is 180cm, one is 190... and the third is 230. Now you've got an average of 2m, which is obviously not true. Let's say you add 2 more people to your sample, both of normal height. (180 and 185) This makes for an average of 193cm, a bit closer to normal. As you get into larger sample sizes this discrepancy from the reading of 230, regardless of whether this is an accurate reading or an error, will get smaller.


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