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Andrea Ortegon's List: Statistics
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Aug 01, 13
"ermining the exact sample size necessary for a study usually requires extensive statistical calculations. However, a reasonable sample size acceptable in most studies utilizes the calculated margin of error. An estimation of margin of error at 95% confidence level (where there is only a 5% chance that the sample results differ from the true population) is given by 1/√N, where N is the number of participants or sample size. This means that a sample size of 10 would have a 31.6% margin of error (1/√10=0.316).
To demonstrate this calculation through example, we can walk-through a study on fear of heights. If researchers survey 10 people and find that 6 respondents are afraid of heights, this means that there is a 95% chance that between 2.8 (6 – 3.16) and 9.2 (6 + 3.16) of the population is actually afraid of heights. With such a large range, the data is not very conclusive. However, if the researchers survey 100 people, the margin of error falls to 10%. Now, if 60 participants report a fear of heights, there is a 95% chance that between 50 (60 – 10) and 70 (60 + 10) of the population actually has a fear of heights. The greater N is, the smaller the margin of error and more useful the measurable results.(7)
In addition to the yield of statistical significance and confidence in results, quality sample size must consider the rate of response. Incomplete or illegible responses are not useful observations. Thus, the total sample size must account for these potential issues.(8)" -
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