on the MCAT for men and women. Write your null hypothesis here. H0: Men MCAT score = Women MCAT score Write your research (alternative) hypothesis here Ha: Men MCAT score ≠ Women MCAT score What two means are you comparing? The two means I would be comparing are the mean MCAT score for men vs. the mean MCAT score for women. Is your test one-tailed or two-tailed? My test is a two-tailed test because the alternative hypothesis is looking at where the MCAT score for men and women are not
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hypothesis. Decision Errors Two types of errors can result from a hypothesis test. Type I error. A Type I error occurs when the researcher rejects a null hypothesis when it is true. The probability of committing a Type I error is called the significance level. This probability is also called alpha‚ and is often denoted by α. Type II error. A Type II error occurs when the researcher fails to reject a null hypothesis that is false. The probability of committing a Type II error is called Beta‚ and is
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the question of how far is far enough. Type I Error Reject a true null hypothesis Considered a serious type of error The probability of a Type I Error is Called level of significance of the test Set by researcher in advance Type II Error Failure to reject a false null hypothesis The probability of a Type II Error is β Type I and Type II errors cannot happen at the same time A Type I error can only occur if H0 is true A Type II error can
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ANALYSIS USING SPSS Overview • Variable • Types of variables Qualitative Quantitative • Reliability and Validity • Hypothesis Testing • Type I and Type II Errors • Significance Level • SPSS • Data Analysis Data Analysis Using SPSS Dr. Nelson Michael J. 2 Variable • A characteristic of an individual or object that can be measured • Types: Qualitative and Quantitative Data Analysis Using SPSS Dr. Nelson Michael J. 3 Types of Variables • Qualitative variables: Variables
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. How does the type of data collected and the way in which the data are collected affect the possibility of a Type I or Type II error? According to Neutens‚ J. J.‚ & Rubinson‚ L. (2010) the key to most significance testing is to establish the extent to which the null hypothesis is believed to be true. The null hypothesis refers to any hypothesis to be nullified and normally presumes chance results only‚ no difference in averages or no correlation between variables. For example‚ if we undertook
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2001 DEVELOPING HYPOTHESIS AND RESEARCH QUESTIONS DEVELOPING HYPOTHESES & RESEARCH QUESTIONS Introduction Processes involved before formulating the hypotheses. Definition Nature of Hypothesis Types How to formulate a Hypotheses in Quantitative Research Qualitative Research Testing and Errors in Hypotheses Summary DEVELOPING HYPOTHESES & RESEARCH QUESTIONS The research structure helps us create research that is : Quantifiable Verifiable Replicable Defensible Corollaries among the
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following question to distinguish between expert control‚ trial and error control‚ intutiative control‚ negotiate control and routine control. When environment changing rapidly‚ tracking performance‚ organization need to detect change by tracking performance‚ scanning the environmental‚ interprating the information detects and responding appropriately. To make it smoothly‚ the operation strategy process should be tracking progress into two type of implementation objective which are project objective and
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success in electronic commerce. Previous research has identified several drivers and impediments to success and the study will mitigate those drivers and impediments to assist an entrepreneur in achieving success as a reseller of tangible goods. Types of Measurement In my research‚ the variables are categorized into two groups: success drivers and impediments to success. The one thing that is common in these items being measured is the fact that they either contribute to success or they inhibit
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DSC 2008 Business Analytics—Data and Decisions Tutorial 1 This first tutorial is longer than usual‚ because it covers 2 weeks of lecture. Since there are frequently no definitive answers to some parts of tutorial questions‚ please only take these files as containing suggested solutions. Some of you might well have different and better insights. In particular‚ your tutor may have different approaches to some questions. Just as not all decisions in real life are correct‚ not all analytics have
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Analysis： For this case‚ we use 0.01 as the significance level. In a hypothesis test‚ a Type I Error occurs when the null hypothesis is rejected when it is in fact true. That means if the consortium decides to consider a settlement when greater than 10% of the patrons resented ads/commercials is true and we reject the null hypothesis when in fact null hypothesis is correct‚ then we will make a type I error. A Type II Error occurs if Ha is true and we accept Ho when it is false. That means if the consortium
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