A partitioning of the power in the one sample Student t-test

The power of Student's t-test is partitioned to determine* the proportion of time the sample standard deviation is too small or the mean is misestimated given that rejection of the hypothesis under test has occurred     

One-sided normal prediction intervals and sample sizes for a third-stage sample

One-sided two-stage prediction intervals for a normal population are extended to a third sampling stage. Procedures and tables are given for two situations. In the first situation, methods for obtaining such intervals are presented, and tables for calculating such prediction intervals are provided. In the second situation, a two-stage prediction interval has been applied, and a third stage is now required. Sample sizes are given for the third stage.     

On Rubbing Raw the Sores of Discontent: Competing Theories and Data on the Effects of Participation in a Black Protest Group

Abstract

Predictions concerning the effects of participation in protest activity stimulated by conflict and resentment are derived from two theories of attitude change, sociotherapy and dissonance reduction theory. These predictions are comparted with the measured attitudes of participants in protest activity in terms of two sets—those who are satisfied with the results and those who aren't. The findings sustain neither the sociotherapists nor their critics. Dissonance reduction theory gains some support in the finding that frustrated participants tend to view the protest group itself less favorably than they view the political system. Though not a direct test of attitude change, the study is a test of the predicted outcomes of conflict stimulation as a community organizing technique and suggests that attitudinal effects are neither cause for concern nor promising for reduction of disaffection in the ghetto.     

Doctoral Education in Social Work: The Decade Ahead

ABSTRACT

Scientific and professional developments in the coming decade will have profound implications for social work doctoral education and the research and academic careers doctoral students are preparing for. These developments include the knowledge explosion, corporatization of the university and related effects, and the emergence of the Grand Challenges for Social Work initiative delimiting key directions for social work research and practice. We consider potential effects of these developments on doctoral social work education and proffer curricular, pedagogic, and programmatic recommendations for improving doctoral education in the coming decade.     

On the Benefit of Inventory‐Based Dynamic Pricing Strategies

We study the optimal pricing and replenishment decisions in an inventory system with a price‐sensitive demand, focusing on the benefit of the inventory‐based dynamic pricing strategy. We find that demand variability impacts the benefit of dynamic pricing not only through the magnitude of the variability but also through its functional form (e.g., whether it is additive, multiplicative, or others). We provide an approach to quantify the profit improvement of dynamic pricing over static pricing without having to solve the dynamic pricing problem. We also demonstrate that dynamic pricing is most effective when it is jointly optimized with inventory replenishment decisions, and that its advantage can be mostly realized by using one or two price changes over a replenishment cycle.     

Variance of the number of false discoveries

Summary.  In high throughput genomic work, a very large number d of hypotheses are tested based on n ≪ d data samples. The large number of tests necessitates an adjustment for false discoveries in which a true null hypothesis was rejected. The expected number of false discoveries is easy to obtain. Dependences between the hypothesis tests greatly affect the variance of the number of false discoveries. Assuming that the tests are independent gives an inadequate variance formula. The paper presents a variance formula that takes account of the correlations between test statistics. That formula involves O ( d ²) correlations, and so a naïve implementation has cost O ( nd ²). A method based on sampling pairs of tests allows the variance to be approximated at a cost that is independent of d .