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It means that the results of the study cannot be claimed to hold for the entire population from which the sample was drawn. The researchers can only claim that their results hold for the individuals selected into their sample.

Q: What effect does non-random sample have on what you can say about the results of a study?

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sample is the population we make our study about them.

Answer D- A higher sample size gives more accurate results- APEX LEARNING

Yes. If the sample is a random drawing from the population, then as the size increases, the relative frequency of each interval from the sample should be a better estimate of the relative frequency in the population. Now, in practical terms, increasing a small sample will have a larger effect than increasing a large sample. For example, increasing a sample from 10 to 100 will have a larger effect than increasing a sample from 1000 to 10,000. The one exception to this, that I can think of, is if the focus of the study is on a very rare occurrence.

businesses would use them to predict trends in data and statisticians would use them to extrapolate the results of a sample group of a study or survey.

cause and effect

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A sample needs to be random and if not a simple random sample of the whole population then a stratified random sample (there are different ways to stratify). Otherwise the study is a waste of time.

(Apex Learning) A higher sample size gives more accurate results.

sample is the population we make our study about them.

Convenience sample Systematic sample Simple random sample (SRS) Census

(Apex Learning) A higher sample size gives more accurate results.

Answer D- A higher sample size gives more accurate results- APEX LEARNING

Answer D- A higher sample size gives more accurate results- APEX LEARNING

Assuming that the population was carefully defined, the sample population was carefully and correctly chosen, and that there were significant results, then the implication is that the results of the study, within the confidence limits indicated, hold true for the population at large.

observational

Voluntary response sample is not generally suitable for statistical study because its results are not likely to be the representative of the entire population under study.Such results could be biased as those who made effort to respond voluntary have strong feelings or opinions whether favorable or unfavorable regarding the subject of consideration.

The more samples you use, the closer your results will match probability.

The deviation would be how much off it would be off. Since this is a sample, it is impossible to get completely accurate results.