Get Common Errors in Statistics (and How to Avoid Them) (4th PDF

By Phillip I. Good, James W. Hardin

ISBN-10: 1118294394

ISBN-13: 9781118294390

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"A very enticing and beneficial publication for all who use information in any setting."

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Common blunders in statistics (and the best way to steer clear of Them), Fourth version presents a mathematically rigorous, but with no trouble obtainable origin in data for skilled readers in addition to scholars studying to layout and entire experiments, surveys, and scientific trials.

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Additional info for Common Errors in Statistics (and How to Avoid Them) (4th Edition)

Sample text

Decreases in any or all of the intrinsic and extrinsic sources of variation will also result in a decrease in the required number. The smallest effect size of practical interest may be determined through consultation with one or more domain experts. The smaller this value, the greater the number of observations that will be required. 1. Ingredients in a sample-size calculation Smallest Effect Size of Practical Interest Type I error (α) Probability of falsely rejecting the hypothesis when it is true.

3. ’s Decision Not a Carcinogen Compound a Carcinogen Not a Carcinogen Carcinogen Type I error: Manufacturer misses opportunity for profit. Public denied access to effective treatment. Type II error: Patients die; families suffer; Manufacturer sued. 4. ) Decline in quality of life Emissions have no effect Economy disrupted Sampling costs worse as it would mean exposing a large number of people to a potentially lethal compound. What losses are associated with the decisions you will have to make? Specify them now before you begin.

Blocking. A clinician might stratify the population into subgroups based on such factors as age, sex, race, and the severity of the condition, and restrict comparisons to individuals who belong to the same subgroup. An agronomist would want to stratify on the basis of soil composition and environment. Blocking can also be performed after the experiment for the purpose of analysis but only if you have taken the time to record the blocking variable. 3. Randomizing. Randomly assign patients to treatment within each subgroup so that the innumerable factors that can neither be controlled nor observed directly are as likely to influence the outcome of one treatment as another.

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Common Errors in Statistics (and How to Avoid Them) (4th Edition) by Phillip I. Good, James W. Hardin

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