'Ii error' presentation slideshows

Statistical Inference for Two Samples

10. Statistical Inference for Two Samples. CHAPTER OUTLINE. 10-1 Inference on the Difference in Means of Two Normal Distributions, Variances Known 10-1.1 Hypothesis tests on the difference of means, variances known 10-1.2 Type II error and choice of sample size

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A Primer for Hypothesis Testing

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Confounding

Confounding. Confounding is an apparent association between disease and exposure caused by a third factor not taken into consideration A confounder is a variable that is associated with the exposure and, independent of that exposure, is a risk factor for the disease. Examples.

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Chapter

Chapter. 10. Hypothesis Tests Regarding a Parameter. Section. 10.1. The Language of Hypothesis Testing. Objectives. Determine the null and alternative hypotheses Explain Type I and Type II errors State conclusions to hypothesis tests. Objective 1.

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Statistical Decision Making

Statistical Decision Making. Analysts must often make decisions about some condition in the real world. Assume that you have finished your MA in policy studies and have been hired as the environmental affairs officer for the city of Morgantown. You must make the following assessment:

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Monday, November 11 Statistical Power

Monday, November 11 Statistical Power. Monday, November 12 Statistical Power It teaches you about the importance of effect size, ? (gamma). Monday, November 12 Statistical Power It teaches you about the importance of effect size, ? (gamma).

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10 November 2003

10 November 2003. 6.2 Tests of Significance 6.3 Use and Abuse of Tests 6.4 Power and Inference as a Decision. Tests of Significance. The reasoning of significance tests Stating hypotheses Test statistics p values Statistical significance Tests for a population mean

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Elementary Statistics

Elementary Statistics. Chapter 1. The Scientific Method. Step 1: Formulate a Theory. Step 2: Collect data to test the theory. Step 3: Analyze the results. Step 4: Interpret the results and make a decision. Definitions.

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Digression - Hypotheses

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By Jessica Jorge

Understanding Power. By Jessica Jorge. Definitions. Type I Error Incorrectly rejecting a true null hypothesis. Type II Error Failing to reject the null hypothesis when it should be rejected. Power Rejecting the null hypothesis when Ha is true THIS IS WHAT YOU WANT.

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Type I and Type II Error

Type I and Type II Error. AP Stat Review, April 18, 2009. Fundamental Outcomes in Hypothesis Tests. As we all (hopefully) remember, results of hypothesis tests fall into one of four scenarios:. Type I Error. OK. OK. Type II Error. Jury Trial vs. Hypothesis Test. Assumption. Defendant is

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CRIM 483

CRIM 483. Statistical Significance. Hypothesis Testing. The null hypothesis assumes that a relationship occurs simply by chance The research hypotheses represents the more extreme outcome

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Unlocking the Mysteries of Hypothesis Testing

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Gage Repeatability and Reproducibility (R&R) Studies

Gage Repeatability and Reproducibility (R&R) Studies. An Introduction to Measurement System Analysis (MSA). Agenda. Importance of data What is MSA? Measurement Error Sources of Variation Precision (Resolution, Repeatability, Reproducibility) Accuracy (Bias, Stability, Linearity)

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Five-step model for Testing Hypotheses

Five-step model for Testing Hypotheses. The five-step model used for all hypothesis tests contains the following steps: Step 1. Evaluating assumptions. Step 2. Stating the research hypothesis and the null hypothesis and setting alpha

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Types of Scaling

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Chapter 13

Chapter 13. Categorical Data Analysis. Categorical Data and the Multinomial Distribution. Properties of the Multinomial Experiment Experiment has n identical trials There are k possible outcomes to each trial, called classes, categories or cells

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Pooled Examples

Statistics . Pooled Examples. A survey of 430 randomly chosen adults found that 21% of the 222 men and 18% of the 208 women purchased books online.

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Budgets: determine the sample size as quickly as possible

Budgets: determine the sample size as quickly as possible. Matthew S. Mayo, Ph.D. Director, Center for Biostatistics and Advanced Informatics Professor and Chair, Department of Biostatistics. The Size of an Experiment. How many patients/animals do we need?

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