Statistical significance within a study determines the confidence the researcher can have in the differences found between two or more variables of that study. In other words, this demonstrates how sure a researcher can be that the relationship found between two variables really does exist. Statistical significance can be tested using various methods, and the significance level at which a variable is detected gives a view of how thoroughly the study has been conducted. This thoroughness often has much to do with the sample size used within an experiment.When a small sample size is used in a given experiment, the numbers that the researcher has to work with are so small that even small portions of that sample will represent larger significances than would be the case with a larger population (Lowry, 2007). A look at two extreme examples will give an idea of how this works. Within a population of two persons, just one of them will represent 50% of the group. However, in a population of 1000 people, 50% is represented by no fewer than 500 people, while one person represents only .01 percent of the population. In a sample containing two persons, statistical analysis cannot be expected to detect differences below 50%. In fact, such an experiment is able only to detect differences (or connections between variables) on a spectrum containing only the proportions represented by 0%, 50% and 100%. No other proportion is possible (Lowry, 2007; StatPac, 2007).On the other hand, in a sample containing 1000 persons or participants, statistical analysis has the ability to detect deviations or connections at a much higher significance level because one person’s deviation represents such a small portion of the population as .01%. Within a population of 500, that same one person would represent .02 of the population, which is twice .01%—and any statistical analysis done on this sample would no longer have the ability to detect significance within a .01 significance level. Therefore, though precisely the same protocol might be followed in all the hypothetical experiments cited above, it is the sample size which ends up determining the ability of the analysis to detect changes at various significance levels.Difference between a statistical and practical significanceIn a large sample size, it is possible to detect very small deviations within the data or very small differences between groups studied. Though it is often useful to make such accurate detections, it is also possible that the very small differences detected will not be very significant in a practical way at all. One example has been given in possible the difference in IQ scores between males and females in a hypothetical test (StatPac, 2007). The ability of the test to detect minute differences between the scores of two groups will become higher once the number of persons within the samples increases. However, if for example the test is able to detect a two point difference between the scores received by males and females, this outcome demonstrates the accuracy of the test but gives results that are insignificant. This insignificance comes from the fact that a two-point difference in IQ makes very little difference in the real world, as a person with an IQ of 110 is likely to be as capable as a person with an IQ of 112. In such a case, it would be more practical to detect a much larger deviation in scores, as that would be much more significant in a practical and useful way (StatPac, 2007).What a statistically significant result denotes is how well the particular variable can be detected by the statistical methods used to analyze it. If a particular relationship exists within a population, yet can only be detected within a small portion of that population, then the result becomes statistically significant at a smaller level or within a smaller confidence interval. Statistical significance speaks about a particular variable and the strength of its relationship to another within a certain population. It is usually left up to the researcher to interpret the value of that relationship and how the knowledge gained from the finding can be of value to the area of research in which the study takes place.ReferencesLowry, R. (2007). “A first glance at the question of statistical significance.” Concepts and         applications of inferential statistics. Vassar College. Retrieved on February 13, 2007   from http://faculty.vassar.edu/lowry//ch4pt1.htmlStatPac. (2007). “Statistical significance.” Designing surveys and questionnaires. Retrieved on February 13, 2007 from http://www.statpac.com/surveys/statistical-significance.htm

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