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Meta-Analysis

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Meta-analysis is the review, and subsequent reanalysis, of relevant data obtained from many studies using statistics, with a view to providing a more coherent conclusion regarding a given hypothesis. The initial compilation of such a study is arduous, requiring many hours of dedicated searching. This in itself is one of the strengths of such a technique, providing somewhat of a fine tooth comb for research already conducted and also allowing for integration of unpublished data which may have been of influence; this will be discussed in more depth later. Studies must be first sourced and validated, and then results can be combined making use of the statistical data; probability (p) and effect size (both will be explained shortly) to test the original hypothesis.

Hypothesis testing relies upon probability (p) to determine whether a positive relationship can be seen between an independent variable (IV) and an independent variable (DV). IV describes the feature that is chosen to test, for example in the hypothesis ‘Does passive smoking increase the chances of lung cancer’ the IV would be passive smoking, therefore the DV would be lung cancer, as it potentially is dependent on the presence of passive smoke. The Null Hypothesis is the ‘no effect’ proposition, in this example ‘ Smoking has no effect on the occurrence of lung cancer’. In order to establish the acceptance of either the Research Hypothesis or the Null Hypothesis the researcher must determine the likelihood of their study conclusions being due to chance; this is where probability comes in. As we can never say for 100% that a study’s result indicates a definite relationship (as we cannot ever test the entire population) probability aims to find the likelihood of a result being found should the Null Hypothesis be true i.e. how likely is it that if 80% of patients with lung cancer had been previously exposed

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