scholarly journals method to streamline p-hacking

2021 ◽  
Vol 5 ◽  
Author(s):  
Ian Hussey
Keyword(s):  
Statistical Inference ◽  
Simulation Study ◽  
Research Funding ◽  
Innovative Approach ◽  
Random Numbers ◽  
Psychological Science ◽  
Widespread Adoption ◽  
Analytic Strategy

The analytic strategy of p-hacking has rapidly accelerated the achievement of psychological scientists’ goals (e.g., publications & tenure), but has suffered a number of setbacks in recent years. In order to remediate this, this article presents a statistical inference measure that can greatly accelerate and streamline the p-hacking process: generating random numbers that are < .05. I refer to this approach as pointless. Results of a simulation study are presented and an R script is provided for others to use. In the absence of systemic changes to modal p-hacking practices within psychological science (e.g., worrying trends such as preregistration and replication), I argue that vast amounts of time and research funding could be saved through the widespread adoption of this innovative approach.

scholarly journals A method to streamline p-hacking

2018 ◽  
Author(s):  
Ian Hussey
Keyword(s):  
Statistical Inference ◽  
Simulation Study ◽  
Research Funding ◽  
Innovative Approach ◽  
Random Numbers ◽  
Inference Method ◽  
Psychological Science ◽  
Widespread Adoption ◽  
Analytic Strategy

The analytic strategy of p-hacking has rapidly accelerated the achievement of psychological scientists’ goals (e.g., publications &amp; tenure), but has suffered a number of setbacks in recent years. In order to remediate this, this article presents a statistical inference method that can greatly accelerate and streamline the p-hacking process: generating random numbers that are &lt; .05. I refer to this approach as pointless. Results of a simulation study are presented and an R script is provided for others to use. In the absence of systemic changes to modal p-hacking practices within psychological science (e.g., worrying trends such as preregistration and replication), I argue that vast amounts of time and research funding could be saved through the widespread adoption of this innovative approach.

Innovative Approach to Generate Uniform Random Numbers Based on a Novel Cellular Automata

2009 ◽  
Vol 9 (22) ◽  
pp. 4071-4075 ◽  
Author(s):  
R. Ayanzadeh ◽  
K. Hassani ◽  
Y. Moghaddas ◽  
H. Gheiby ◽  
S. Setayeshi
Keyword(s):  
Cellular Automata ◽  
Innovative Approach ◽  
Random Numbers ◽  
Uniform Random Numbers

scholarly journals Overlap coefficients based on Kullback-Leibler divergence: Exponential populations case

2017 ◽  
Vol 6 (4) ◽  
pp. 135
Author(s):  
Hamza Dhaker ◽  
Papa Ngom ◽  
Malick Mbodj
Keyword(s):  
Statistical Inference ◽  
Mean Square Error ◽  
Confidence Intervals ◽  
Taylor Series ◽  
Simulation Study ◽  
Invariance Property ◽  
Mean Square ◽  
Taylor Series Approximation ◽  
Leibler Divergence ◽  
Exponential Populations

This article is devoted to the study of overlap measures of densities of two exponential populations. Various Overlapping Coefficients, namely: Matusita’s measure ρ, Morisita’s measure λ and Weitzman’s measure ∆. A new overlap measure Λ based on Kullback-Leibler measure is proposed. The invariance property and a method of statistical inference of these coefficients also are presented. Taylor series approximation are used to construct confidence intervals for the overlap measures. The bias and mean square error properties of the estimators are studied through a simulation study.

It's a Girl! Random Numbers, Simulations, and the Law of Large Numbers

2015 ◽  
Vol 20 (9) ◽  
pp. 561-564
Author(s):  
Chris Goodwin ◽  
Enrique Ortiz
Keyword(s):  
Statistical Inference ◽  
Law Of Large Numbers ◽  
Descriptive Statistics ◽  
Random Numbers ◽  
Fields Of Study ◽  
Large Numbers ◽  
The Law

Modeling using mathematics and making inferences about mathematical situations are becoming more and more prevalent in most fields of study. When we want to generalize about a population or make predictions of what could occur, we cannot use descriptive statistics. Instead, we turn to inference. Simulation and sampling are essential in building a foundation for statistical inference.

SHARP BOUNDS ON THE DISTRIBUTION OF TREATMENT EFFECTS AND THEIR STATISTICAL INFERENCE

2009 ◽  
Vol 26 (3) ◽  
pp. 931-951 ◽  
Author(s):  
Yanqin Fan ◽  
Sang Soo Park
Keyword(s):  
Sample Size ◽  
Statistical Inference ◽  
Confidence Intervals ◽  
Simulation Study ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Asymptotic Distributions ◽  
Finite Sample ◽  
Sharp Bounds ◽  
Nonparametric Estimators

In this paper, we propose nonparametric estimators of sharp bounds on the distribution of treatment effects of a binary treatment and establish their asymptotic distributions. We note the possible failure of the standard bootstrap with the same sample size and apply the fewer-than-nbootstrap to making inferences on these bounds. The finite sample performances of the confidence intervals for the bounds based on normal critical values, the standard bootstrap, and the fewer-than-nbootstrap are investigated via a simulation study. Finally we establish sharp bounds on the treatment effect distribution when covariates are available.

scholarly journals Mathematical Programming for Statistical Inference

2018 ◽  
pp. 1-8
Author(s):  
Abeer M. M. Elrefaey ◽  
Ramadan Hamid ◽  
Elham A. Ismail ◽  
Safia M. Ezzat
Keyword(s):  
Quality Control ◽  
Mathematical Programming ◽  
Statistical Inference ◽  
Simulation Study ◽  
Type I Error ◽  
Programming Models ◽  
Type I ◽  
Mathematical Programming Models ◽  
Level Of Significance ◽  
Applied Fields

The study is concerned with the transforming theoretical Mathematical models into applied Mathematical programming models that are easy to handle and use. These Mathematical programming models can be applied and used in statistical inference, which used in many applied fields, for example, quality control and its application. The aim of this paper is to suggest two mathematical programming models for hypotheses tests, which make a balance between the high power (1-β), and the probability of a type I error, significance (), of the test. The paper introduces a simulation study to evaluate the performance of the two suggested mathematical programming models for tests hypotheses. The two suggested mathematical programming models solved with different sample sizes and different level of significance. The suggested models calculate the critical values which determine the rejection region exactly and the results are easy to interpret clearly. Then the conclusion for the suggested mathematical programming models makes balance between the power and the significance.  

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