Corpus ID: 124212083

Power Analysis Using R

  title={Power Analysis Using R},
  author={Simon P. Blomberg},
Thus, a rational approach to hypothesis testing will seek to reject a hypothesis if it is false and accept a hypothesis when it is true. The two types of error are rejecting the null hypothesis when it is true (Type I) and accepting the null hypothesis when it is false (Type II). In the Neyman-Pearson theory, it is usual to fix the Type I error probability (α) at some constant (often at 0.05, but not necessarily), and then choose a test which minimises the Type II error probability… Expand

Tables from this paper


In an earlier paper* we have endeavoured to emphasise the importance of placing in a logical sequence the stages of reasoning adopted in the solution of certain statistical problems, which may beExpand
The testing of statistical hypotheses in relation to probabilities a priori
What statements of value to the statistician in reaching his final judgment can be made from an analysis of observed data, which would not be modified by any change in the probabilities a priori, are discussed. Expand
The Abuse of Power
It is well known that statistical power calculations can be valuable in planning an experiment. There is also a large literature advocating that power calculations be made whenever one performs aExpand
On the Problem of the Most Efficient Tests of Statistical Hypotheses
The problem of testing statistical hypotheses is an old one. Its origins are usually connected with the name of Thomas Bayes, who gave the well-known theorem on the probabilities a posteriori of theExpand
Final Collapse of the Neyman-Pearson Decision Theoretic Framework and Rise of the neoFisherian
This essay grew out of an examination of one-tailed significance testing. One-tailed tests were little advocated by the founders of modern statistics but are widely used and recommended nowadays inExpand
The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives
"Statistical significance," a technique that dominates medicine, economics, psychology, and many other scientific fields, has been a huge mistake. The outcome is a case study in bad science - how itExpand
Testing Statistical Hypotheses
This classic textbook, now available from Springer, summarizes developments in the field of hypotheses testing. Optimality considerations continue to provide the organizing principle. However, theyExpand
Regression analysis of multivariate incomplete failure time data by modeling marginal distributions
Abstract Many survival studies record the times to two or more distinct failures on each subject. The failures may be events of different natures or may be repetitions of the same kind of event. InExpand
Statistical Power Analysis for the Behavioral Sciences
Contents: Prefaces. The Concepts of Power Analysis. The t-Test for Means. The Significance of a Product Moment rs (subscript s). Differences Between Correlation Coefficients. The Test That aExpand
The Significance of Fisher: A Review of R.A. Fisher: The Life of a Scientist
Abstract A number of colleagues have made helpful criticism and comments. They certainly do not uniformly agree with my judgments and emphases, but my warm appreciation goes to Keith Baker, AlbertExpand