I will assume the sample is random. In general, the larger the sample, the smaller the percentage error will be (the difference between percentages in the sample, and the percentages in the universe from whence the sample is taken). The percentage error tends to go down as the square root of the size of the sample.
A small sample is sufficient provided that it is representative: that is, the sample units are chose randomly.
Because then you can assess how valid your results are =D
The answers are usually always valid. What may or may not be valid are your assumptions about the underlying model. Also, the number of times the results should be similar depends on the number of possible outcomes and the variability in the outcomes. For example, if you spin a fair spinner with 12 equal segments, then the probability of similar results is less than likely.
Because without representative sample, your results will not be valid.
validity is whether the results are valid so the data has no mistakes of as such in it whereas reliability is the dependability; when the results you have are accurate and are of enough quality.
If you documented all your results, had a partner, had a witness, completed the experiment many times with the same results, and tested the experiment on the proper things then this would be good validation.
Statistically the results will not be scientifically valid if the sample size is too small.
Check if the results are valid
a valid investigation is an effective investigation i think. The results turn out to be what you had inferred.
Repeatable
Sampling techniques can provide statistically reliable and valid survey results except haphazard sampling.
booty
repeated trials
To make an experiment valid you have to make sure that the experiments results answer the question that you first started from.
A test may be reliable in that the methods, calculations, measurements and equipment are all correct. However, if one of the samples is flawed, the test would not be valid.
A valid passport is required
valid
A test may be reliable yet not valid, The results can end up being reliable, in other words certain to have yielded properly based on input. But the results may not be trustworthy.