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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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How can a large sample sizre help minimize instrument error in an experiment?
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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A large sample will reduce the effects of random variations.
Why is it necessary when performing an experiment that a large quantity of tests and trials are performed?
It necessary when performing an experiment that a large quantity of tests and trials are performed because observations tend to have errors. There are random errors, which are errors in measurement, and there are systematic errors, which are errors in procedure or calibration. Both have to be considered. Performing the experiment more than once allows one to estimate the varience of the results, to reject erroneous results, and to more accurately describe how the results fit the theory.
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Disadvantages of a large sample size confidence Interval in statistices?
A disadvantage to a large sample size can skew the numbers. It is better to have sample sizes that are appropriate based on the data.
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large
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Because the whole population might be too large to sample. A good example is the population of the world. At nearly 7 billion people, it would be unrealistic to sample each person to determine some factor that you are looking at. Generally, we sample a subset of the population, taking into account differences (or errors) that might result, in this case, regional and cultural, in order to estimate the behavior of the larger population.
What is used to create a large sample of DNA for testing for a small sample of DNA?
PCR
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What two features must a sample have if its to accurately represent a population?
The sample must be large and random.
