Data Analysis Methods Used in Quantitative Research.

4348 WordsMar 18, 201118 Pages
DEFINITION: Quantitative methods are research techniques that are used to gather quantitative data — information dealing with numbers and anything that is measurable e.g. Statistics, tables and graphs, are often used to present the results of these methods. Quantitative research methods were originally developed in the natural sciences to study natural phenomena. However examples of quantitative methods now well accepted in the social sciences and education. Differences between parametric and non parametric methods Parametric method Non parametric Assumes data comes from a type of probability distributions ( they are not distribution-free) Distribution free methods which do not rely on assumptions that the data are drawn from a…show more content…
To answer this question, the decision maker or statistician must reorganize that any such test procedure or decision rule can make either a type I error or a type II error. in this case, a Type I error will arise if the firm rejects a shipment in which the mean length of life of the bulbs is 2000 hours a Type II error will arise if the firm does not reject a shipment where the mean length of life of the bulbs is less than 2000 hours. - The possibility in this case , much as the possibility of type I error is designated α (alpha) and the possibility of a Type II error is designated as β (beta) - Possible outcomes of the decision problem concerning the shipment of the light bulbs. Table: Possible outcomes of the decision problem concerning the shipment of light Bulbs Alternative courses of action μ=2000 State of nature μ < 2000 Do not reject shipment Reject shipment Correct decision Type I error Type II error correct decision If μ >2000, the correct decision is to not reject the shipment 2 Specify the significance level of the test The value assigned to α is called the significance level of the test. Typically, the significance level is set at 0.05 or 0.01, which means that the probability of type I error is 0.05 or 0.01 The decision maker can set the significance level at any amount that he chooses. However, in setting this level it generally wise to keep in mind the relative costs of
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