Search results for "significance testing"
showing 8 items of 8 documents
Elements of Significance Testing with Equivalence Problems
1991
AbstractThe paper outlines an approach to the general methodological problem of equivalence assessment which is based on the classical theory of testing statistical hypotheses. Within this frame of reference it is natural to search for decision rules satisfying the same criteria of optimality which are customarily applied in deriving solutions to one- and two-sided testing problems. For three standard situations very frequently encountered in medical applications of statistics, a concise account of such an optimal test for equivalence is presented. It is pointed out that tests based on the well-known principle of confidence interval inclusion are valid in the sense 1 of guaranteeing the pre…
Corrigendum: ExGUtils: A Python Package for Statistical Analysis With the ex-Gaussian Probability Density
2018
The study of reaction times and their underlying cognitive processes is an important field in Psychology. Reaction times are usually modeled through the ex-Gaussian distribution, because it provides a good fit to multiple empirical data. The complexity of this distribution makes the use of computational tools an essential element in the field. Therefore, there is a strong need for efficient and versatile computational tools for the research in this area. In this manuscript we discuss some mathematical details of the ex-Gaussian distribution and apply the ExGUtils package, a set of functions and numerical tools, programmed for python, developed for numerical analysis of data involving the ex…
Manipulating the alpha level cannot cure significance testing
2018
We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple in…
Statistical inference in abstracts of 3 influential clinical pharmacology journals analysed using a text‐mining algorithm
2021
Aim To describe the trend in the prevalence of statistical inference in three influential clinical pharmacology journals METHODS: We applied a computer-based algorithm to abstracts of three clinical pharmacology journals published in 1976 to 2016 to identify statistical inference and its subtypes. Furthermore, we manually reviewed a random sample of 300 articles to access algorithm's performance in finding statistical inference in abstracts and as a screening tool for presence and absence of statistical inference in full text. Result The algorithm identified 59% (13,375/22,516 [mid p 95% CI, 59%-60%]) article abstracts with statistical inference. The percentage of abstracts with statistical…
Predicting the Significance of Necessity
2019
With Necessary Condition Analysis (NCA), a necessity effect is estimated by calculating the amount of empty space in the upper-left corner in a plot with a predictor X and an outcome Y, and recently a method for testing the statistical significance of the necessity effect through permutation has been proposed. In the present simulation study, this method was found to give significant results already with a very weak true population necessity effect, i.e., exhibit high power, unless the sample size is very small. However, in some situations the significance of the necessity effect tends to increase with increased degree of sufficiency, which is paradoxical for a method whose objective is to …
P-Value, Confidence Intervals, and Statistical Inference: A New Dataset of Misinterpretation
2017
Statistical inference is essential for science since the twentieth century (Salsburg, 2001). Since it's introduction into science, the null hypothesis significance testing (NHST), in which the P-value serves as the index of “statistically significant,” is the most widely used statistical method in psychology (Sterling et al., 1995; Cumming et al., 2007), as well as other fields (Wasserstein and Lazar, 2016). However, surveys consistently showed that researchers in psychology may not able to interpret P-value and related statistical procedures correctly (Oakes, 1986; Haller and Krauss, 2002; Hoekstra et al., 2014; Badenes-Ribera et al., 2016). Even worse, these misinterpretations of P-value …