Statistical hypothesis testing

About: Statistical hypothesis testing is a research topic. Over the lifetime, 19580 publications have been published within this topic receiving 1037815 citations. The topic is also known as: statistical hypothesis testing & confirmatory data analysis.

Estimating the autocorrelated error model with trended data, further results

Abstract: : A Monte Carlo study is made of the small sample properties of various estimators of the linear regression model with first-order autocorrelated errors. When independent variables are trended, estimators using T transformed observations (Prais-Winsten) are much more efficient than those using T-1 (Cochrane-Orcutt). The best of the feasible estimators is iterated Prais-Winsten using a sum-of-squared-error minimizing estimate of the autocorrelation coefficient rho. None of the feasible estimators performs well in hypothesis testing; all seriously underestimate standard errors, making estimated coefficients appear to be much more significant than they actually are. (Author)

Statistical Mediation Analysis

Abstract: Hypotheses regarding how an independent variable affects a dependent variable via a mediating variable are widespread in both basic and applied psychology. This chapter focuses on statistical and design methods to investigate mediation relations rather than the substantive importance of mediation that is described elsewhere (MacKinnon, 2008). The chapter starts with detailed information on the single mediator model including covariance between estimates, measures of effect size, hypothesis testing, confidence limit estimation, and Bayesian methods. Causal inference approaches for mediation are described. Comprehensive mediation models are then discussed including models that accommodate both moderator and mediator variables, multiple mediators, multilevel models, and models that incorporate longitudinal relations among variables. We acknowledge that the identification of mediating variables can be a challenging process requiring a variety of information in addition to statistical analysis such as replication and experimental studies. Although the identification of mediating variables is a challenging task, many new statistical and methodological tools have been developed to help researchers. Keywords: data analysis; mediating variables; mediation analysis; statistics

Procedures for two-sample comparisons with multiple endpoints controlling the experimentwise error rate.

Abstract: Clinical trials are often concerned with the comparison of two treatment groups with multiple endpoints. As alternatives to the commonly used methods, the T2 test and the Bonferroni method, O'Brien (1984, Biometrics 40, 1079-1087) proposes tests based on statistics that are simple or weighted sums of the single endpoints. This approach turns out to be powerful if all treatment differences are in the same direction [compare Pocock, Geller, and Tsiatis (1987, Biometrics 43, 487-498)]. The disadvantage of these multivariate methods is that they are suitable only for demonstrating a global difference, whereas the clinician is further interested in which specific endpoints or sets of endpoints actually caused this difference. It is shown here that all tests are suitable for the construction of a closed multiple test procedure where, after the rejection of the global hypothesis, all lower-dimensional marginal hypotheses and finally the single hypotheses are tested step by step. This procedure controls the experimentwise error rate. It is just as powerful as the multivariate test and, in addition, it is possible to detect significant differences between the endpoints or sets of endpoints.