24 papers · ranked by Valyu relevance
Jian Gao
Background In medical research and practice, the p-value is arguably the most often used statistic and yet it is widely misconstrued as the probability of the type I error, which comes with serious consequences. This misunderstanding can greatly affect the reproducibility in research, treatment selection in medical…
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…
Shraddha Parab, Supriya Bhalerao
In terms of selecting a statistical test, the most important question is “what is the main study hypothesis?” In some cases there is no hypothesis; the investigator just wants to “see what is there”. For example, in a prevalence study, there is no hypothesis to test, and the size of the study is determined by how…
Anish Acharya
The paper titled 'Controlling the False Discovery Ratea practical and powerful Approach to multiple Testing' by benjamini et. al.[1] proposes a new framework of controlling the False Discovery Rate in a Multiple Hypothesis testing problem. It has been claimed that the procedure proposed in the paper results in a…
Victor Coscrato, Luís Gustavo Esteves, Rafael Izbicki, Rafael B. Stern
'Rafael B. Stern'] Abstract: Although hypothesis tests play a prominent role in Science, their interpretation can be challenging. Three issues are (i) the difficulty in making an assertive decision based on the output of an hypothesis test, (ii) the logical contradictions that occur in multiple hypothesis testing, and…
Richard McNulty
Background Null Hypothesis Significance Testing (NHST) has been well criticised over the years yet remains a pillar of statistical inference. Although NHST is well described in terms of statistical models, most textbooks for non-statisticians present the null and alternative hypotheses (H0 and HA, respectively) in…
Mark A. Rubin
Scientists often adjust their significance threshold (alpha level) during null hypothesis significance testing in order to take into account multiple testing and multiple comparisons. This alpha adjustment has become particularly relevant in the context of the replication crisis in science. The present article…
J Perezgonzalez
Despite frequent calls for the overhaul of null hypothesis significance testing (NHST), this controversial procedure remains ubiquitous in behavioral, social and biomedical teaching and research. Little change seems possible once the procedure becomes well ingrained in the minds and current practice of researchers…
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…
Denis Cousineau
Null hypothesis statistical testing (NHST) is typically taught by first posing a null hypothesis and an alternative hypothesis. This conception is sadly erroneous as there is no alternative hypothesis in the NHST. This misconception generated erroneous interpretations of the NHST procedures, and the fallacies that were…
David Colquhoun
We wish to answer this question If you observe a “significant” P value after doing a single unbiased experiment, what is the probability that your result is a false positive?. The weak evidence provided by P values between 0.01 and 0.05 is explored by exact calculations of false positive rates. When you observe P =…
Nicolas Meyer, Stefanos Bonovas
Almost all publications in biomedical literature have employed statistical tests, with p-values being considered of particular importance in the assessment of the presence of a link between two variables. However, these tests and p-values have been the subject of considerable criticism. It may appear paradoxical that…
Priyantha Wijayatunga
Jeffreys–Lindley paradox is a case where frequentist and Bayesian hypothesis testing methodologies contradict with each other. This has caused confusion among data analysts for selecting a methodology for their statistical inference tasks. Though the paradox goes back to mid 1930's so far there hasn't been a…
Yunxiao Li, Yi-Juan Hu, Glen A. Satten
The use of Monte-Carlo (MC) p-values when testing the significance of a large number of hypotheses is now commonplace. In large-scale hypothesis testing, we will typically encounter at least some p-values near the threshold of significance, which require a larger number of MC replicates than p-values that are far from…
Jose D. Perezgonzalez
Despite frequent calls for the overhaul of null hypothesis significance testing (NHST), this controversial procedure remains ubiquitous in behavioral, social and biomedical teaching and research. Little change seems possible once the procedure becomes well ingrained in the minds and current practice of researchers…
Jesse Hemerik
This text provides an introduction to multiple hypothesis testing. It covers various error criteria and testing procedures, and includes references to relevant R packages. An earlier version of this text served as the lecture notes for a PhD-level course on multiple testing.
Reid Dale
Confirmation Authors: ['Reid Dale'] Abstract. Null Hypothesis Statistical Testing is a dominant framework for conducting statistical analysis across the sciences. There remains considerable debate as to whether, and under what circumstances, evidence can be said to be confirmatory of a null hypothesis. This paper…
Denes Szucs, John PA Ioannidis
Null hypothesis significance testing (NHST) has several shortcomings that are likely contributing factors behind the widely debated replication crisis of psychology, cognitive neuroscience and biomedical science in general. We review these shortcomings and suggest that, after about 60 years of negative experience, NHST…
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…
Authors not listed
The exponentially growing body of scientific literature has made manual synthesis and hypothesis generation increasingly impractical, introducing an essential bottleneck in the scientific discovery pipeline. While large language models (LLMs) have unprecedented capability to process and summarize textual knowledge…
Authors not listed
High-throughput experimentation (HTE) in materials science generates vast, high-dimensional datasets relating synthesis parameters to material properties. While machine learning (ML) models excel at predicting properties from these parameters, they often fail to distinguish causal drivers from merely correlated…
Authors not listed
Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…
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…
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…