By Dr. Phillip I. Good, James W. Hardin(auth.)
Chapter 1 resources of errors (pages 1–11):
Chapter 2 Hypotheses: The Why of Your examine (pages 13–25):
Chapter three gathering info (pages 27–43):
Chapter four Estimation (pages 45–56):
Chapter five checking out Hypotheses: deciding upon a attempt Statistic (pages 57–85):
Chapter 6 Strengths and obstacles of a few Miscellaneous Statistical tactics (pages 87–100):
Chapter 7 Reporting Your effects (pages 101–117):
Chapter eight examining stories (pages 119–124):
Chapter nine pics (pages 125–144):
Chapter 10 Univariate Regression (pages 145–162):
Chapter eleven trade tools of Regression (pages 163–173):
Chapter 12 Multivariable Regression (pages 175–186):
Chapter thirteen Validation (pages 187–194):
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Additional info for Common Errors in Statistics (and How to Avoid Them), Second Edition
Every research study involves multiple issues. We might want to know not only whether a measurable, biologically (or medically, physically, or sociologically) significant effect takes place but also what the size of the effect is and the extent to which the effect varies from instance to instance. We would also want to know what factors, if any, will modify the size of the effect or its duration. We may not be able to address all these issues with a single data set. A preliminary experiment might tell us something about the possible existence of an effect, along with rough estimates of its size and variability.
Then and only then will we hope to get the answers to all our research questions. Before you begin, list all the consequences of a study and all the actions you might take. Persist only if you can add to existing knowledge. TO LEARN MORE For more thorough accounts of decision theory, the interested reader is directed to Berger (1986), Blyth (1970), Cox (1958), DeGroot (1970), and Lehmann (1986). For an applied perspective, see Clemen (1991), Berry (1995), and Sox, Blatt, Higgins, and Marton (1988).
The bias-corrected and accelerated BCa interval due to Efron and Tibshirani (1986) also represents a substantial improvement, although for samples under size 30, the interval is still suspect. The idea behind these intervals comes from the observation that percentile bootstrap intervals are most accurate when the estimate is symmetrically distributed about the true value of the parameter and the tails of the estimate’s distribution drop off rapidly to zero. 1 represents this ideal. Suppose θ is the parameter we are trying to estimate, θˆ is the estimate, and we are able to come up with a monotone increasing transformation m such that m(θ) is normally distributed about m(θˆ ).