24 papers · ranked by Valyu relevance
Asghar Ghasemi, Saleh Zahediasl
Statistical errors are common in scientific literature and about 50% of the published articles have at least one error. The assumption of normality needs to be checked for many statistical procedures, namely parametric tests, because their validity depends on it. The aim of this commentary is to overview checking for…
Shraddha Parab, Supriya Bhalerao
The most important statistical tests are listed in [T0001]. A distinction is always made between “categorical or continuous” and “paired or unpaired.” The group comparison for two categorical endpoints is illustrated here with the simplest case of a 2 × 2 table (four-field table) [[F0001]]. However, the procedure is…
Younis Skaik
Statistical tests are very important in biomedical research.1 Several factors play a role in selecting the most appropriate statistical test.2 The misuse or inaccurate use of a statistical test may navigate the research in the wrong direction, and hence incorrect conclusions. Because it is probably the most commonly…
Markku Kuismin
A new method based on the rejection sampling for finding statistical tests is proposed. This method is conceptually intuitive, easy to implement, and applicable for arbitrary dimension. To illustrate its potential applicability, three distinct empirical examples are presented: (1) examine the differences between group…
Justine Rochon, Matthias Gondan, Meinhard Kieser
Background Student’s two-sample t test is generally used for comparing the means of two independent samples, for example, two treatment arms. Under the null hypothesis, the t test assumes that the two samples arise from the same normally distributed population with unknown variance. Adequate control of the Type I error…
Vladimir Trkulja, Pero Hrabač
The Student's t test In previous columns, we touched on certain concepts in statistics that form the basis of statistical thinking. In this text, we will deviate briefly from the general concepts and focus on a single statistical test. Namely, we will discuss the basics of the t test, known also as the Student's t…
A. Banerjee, S. L. Jadhav, J. S. Bhawalkar
Few clinicians grasp the true concept of probability expressed in the ‘P value.’ For most, a statistically significant P value is the end of the search for truth. In fact, the opposite is the case. The present paper attempts to put the P value in proper perspective by explaining different types of probabilities, their…
Daniel Beasley
Randomized controlled trials (RCTs) are the gold standard for inferring causal relationships between treatments and effects. They are widely applied by scientists to deepen understanding of their disciplines. Within the past two decades, they have found applications in digital products as well, under the name A/B test.…
Carol Ting
It has long been a puzzle why, despite sustained reform efforts, many applied scientific fields remain dominated by Null Hypothesis Significance Testing (NHST), a framework that dichotomizes study results and privileges "statistically significant" findings. This paper examines that puzzle by situating the development…
Authors not listed
We developed OpenStats, a user-friendly web application that brings the power of the R language to researchers through a high-level interface and broad support for statistical methods such as t-tests and ANOVA. OpenStats was integrated into our electronic lab notebook Chemotion ELN via its third-party API, enabling…
Eric Maris
The statistical analysis of biological data is mostly performed in a parametric framework. One of the problems with this approach is that the false alarm (FA) rate of these parametric statistical tests (the probability of falsely rejecting the null hypothesis) often cannot be controlled (2). Besides other advantages…
Md Rejuan Haque, Laura Kubatko
Combination tests are used to combine P-values from individual studies to test a global null hypothesis. These types of tests can also be applied to combine P-values from testing separate null hypotheses within the same study in cases for which the procedure for testing a global null hypothesis is unavailable. One such…
Aaron F. McDaid, Zoltán Kutalik, Valentin Rousson
A statistical test can be seen as a procedure to produce a decision based on observed data, where some decisions consist of rejecting a hypothesis (yielding a significant result) and some do not, and where one controls the probability to make a wrong rejection at some pre-specified significance level. Whereas…
Orestis Loukas, Ho‐Ryun Chung
Statistical hypothesis testing is the central method to demarcate scientific theories in both exploratory and inferential analyses. However, whether this method befits such purpose remains a matter of debate. Established approaches to hypothesis testing make several assumptions on the data generation process beyond the…
Rand R. Wilcox, Guillaume A. Rousselet
There is a vast array of new and improved methods for comparing groups and studying associations that offer the potential for substantially increasing power, providing improved control over the probability of a Type I error, and yielding a deeper and more nuanced understanding of neuroscience data. These new techniques…
Authors not listed
The precision of thermodynamic modeling for ionic liquid (IL)–solute systems is fundamentally reliant on the quality of experimental data. However, prevalent databases such as ILThermo frequently exhibit conflicting measurements for the same systems under identical temperature and pressure conditions. These disparities…
Tommy Clausner, Stefano Gentili
In the present paper we propose a non-parametric statistical test procedure for interval scaled, paired samples data that circumvents the multiple comparison problem (MCP) by relating the data to the rank order of its group averages. Using an auto-regressive procedure, a single test statistic for multiple groups is…
Jennifer A. Sinnott, Steven N. MacEachern, Mario Peruggia
We discuss the role that the null hypothesis should play in the construction of a test statistic used to make a decision about that hypothesis. To construct the test statistic for a point null hypothesis about a binomial proportion, a common recommendation is to act as if the null hypothesis is true. We argue that, on…
D. Samuel Schwarzkopf
The problems with classical frequentist statistics are well established, yet the enthusiasm of researchers to adopt alternatives like Bayesian inference remains modest. Here I present the bootstrapped evidence test, an objective resampling procedure that takes the precision with which both the experimental and null…
Jesper Tijmstra
This article explores whether the null hypothesis significance testing (NHST) framework provides a sufficient basis for the evaluation of statistical model assumptions. It is argued that while NHST-based tests can provide some degree of confirmation for the model assumption that is evaluated-formulated as the null…
Estibaliz Gómez-de-Mariscal, Vanesa Guerrero, Alexandra Sneider, Hasini Jayatilaka + 3 more
Biomedical research has come to rely on p-values as a deterministic measure for data-driven decision making. In the largely extended null-hypothesis significance testing (NHST) for identifying statistically significant differences among groups of observations, a single p-value computed from sample data is routinely…
Conrad Hübler
A novel application to determine stability constants from supramolecular titration experiments is presented. The focus lies on NMR titration and ITC experiments for pure 1:1 systems, as well as mixed 2:1/1:1, 1:1/1:2 and 2:1/1:1/1:2 systems. SupraFit provides global and local fitting and a global search tool.…
Authors not listed
Mechanical agitation (stirring) is a cornerstone of organic synthesis, but has received little scientific attention due to its “obvious” role in facilitating reactions. A very recent study by Huang and coworkers compared the isolated yields in approximately 600 paired stirred and unstirred reactions, across a range of…
Finlay Clark, Graeme Robb, Daniel Cole, Julien Michel
Alchemical absolute binding free energy (ABFE) calculations have substantial potential in drug discovery, but are often prohibitively computationally expensive. To unlock their potential, efficient automated ABFE workflows are required to reduce both computational cost and human intervention. We present a…