14++Inferential+Statistics

14++Inferential+Statistics - IntroductiontoInferential...

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Introduction to Inferential  Statistics Or how I learned to realize that Dr. Sandwina is not nuts, just horribly skewed.
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Uses of Inferential Statistics Inferential Statistics are used to make an inference, or generalization, about a population from an sample . Specifically, we use inferential statistics to: Test hypotheses Determine if a relationship exists between two or more variable.
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Hypotheses Testing 1. Specify the hypothesis 2. Specify a significance level (also known as alpha or α). 3. Calculate the appropriate statistic and probability levels 4. Determine whether the null hypothesis was rejected
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Step 1: Specify the hypotheses Hypothesis defined: a statement of a relationship between two or more variables. It predicts how the variables are related.
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Two types of hypotheses A null hypothesis is a statement of no relationship between variables A null hypothesis is indicated by The researcher hopes to reject the null hypothesis. A research hypothesis , also known as the alternative hypothesis, is a statement that there is a relationship between variables. A research hypothesis is indicated by or The researcher hopes to accept or confirm the research hypothesis o H A H 1 H
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The research question When researchers believe that two or more variables are related to each other, but are unsure how the variables are related to each other, they do not state a hypothesis; instead the pose a research question, which is indicated by RQ
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Step 2: Specify a significance level  The significance level represents how confident we are about our predictions. The typical significance level or confidence level is set at .05, which means we expect our findings to occur 95 times out of 100. Chance accounts for the other 5 percent. The researcher has to decide whether to conduct a one‑tailed test or a two‑tailed test.
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More about significance level Significance level, alpha (or α) and probability level all refer to the same concept. Research studies typically report both the alpha level and the “ p ” value, which is the actual probability generated by their statistical test of the hypothesis. Now, let’s talk about the difference between a one-tail or two-tail test.
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Two-Tailed versus One-Tailed Test If you are investigating if a difference exists, but you can not predict that the difference will necessarily be in one direction, then you would use a two-tailed test . If you expect that a difference will be in only one direction, then you would use a one-tailed test . A one tailed test gives you more power to detect difference because the region of rejection is larger.
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Example There are two sections of G310. One section has only seniors; the other section has only freshmen. Here is the distribution of scores for an exam for the senior only section. Can you predict where the distribution of scores
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14++Inferential+Statistics - IntroductiontoInferential...

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