By Dr. Phillip I. Good, James W. Hardin(auth.)
Chapter 1 assets of errors (pages 1–11):
Chapter 2 Hypotheses: The Why of Your learn (pages 13–25):
Chapter three accumulating information (pages 27–43):
Chapter four Estimation (pages 45–56):
Chapter five checking out Hypotheses: making a choice on a attempt Statistic (pages 57–85):
Chapter 6 Strengths and boundaries of a few Miscellaneous Statistical strategies (pages 87–100):
Chapter 7 Reporting Your effects (pages 101–117):
Chapter eight reading reviews (pages 119–124):
Chapter nine pix (pages 125–144):
Chapter 10 Univariate Regression (pages 145–162):
Chapter eleven trade equipment of Regression (pages 163–173):
Chapter 12 Multivariable Regression (pages 175–186):
Chapter thirteen Validation (pages 187–194):
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Additional resources for Common Errors in Statistics (and How to Avoid Them), Second Edition
In short, randomization alone, without allocation concealment, is insufficient to eliminate selection bias and ensure the internal validity of randomized clinical trials. Lovell et al. (2000) describe a study in which four patients were randomized to the wrong stratum and, in two cases, the treatment received was reversed. pdf. Vance Berger and Costas Christophi offer the following guidelines for treatment allocation: • Generate the allocation sequence in advance of screening any patients. • Conceal the sequence from the experimenters.
The same problems will arise if you discover during the course of a study (as is often the case) that a precise measuring instrument is no longer calibrated and readings have drifted. To forestall this, any measurCHAPTER 3 COLLECTING DATA 37 ing instrument should have been exposed to an extensive burn-in before the start of a set of experiments and should be recalibrated as frequently as the results of the burn-in or prestudy period dictate. Similarly, one doesn’t just mail out several thousand copies of a survey before performing an initial pilot study to weed out or correct ambiguous and misleading questions.
This interval provides us with an estimate of the precision of the corresponding point estimate. We resample with replacement repeatedly from the original sample, 1000 times or so, computing the sample statistic for each bootstrap sample. For example, here are the heights of a group of 22 adolescents, measured in centimeters and ordered from shortest to tallest. 5 The median height lies somewhere between 153 and 154 centimeters. If we want to extend this result to the population, we need an estimate of the precision of this average.
Common Errors in Statistics (and How to Avoid Them), Second Edition by Dr. Phillip I. Good, James W. Hardin(auth.)