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Bias

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...

Decision Making: P-value Method

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...

Bias in Epidemiological Studies

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:

Accuracy and Errors in Hypothesis Testing

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...

Statistical Hypothesis Testing

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

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Post-Movie Subliminal Measurement PMSM, for Investigating Implicit Social Bias

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Modelling publication bias and p-hacking.

Jonas Moss¹, Riccardo De Bin¹

¹Department of Mathematics, University of Oslo, Oslo, Norway.

|September 12, 2021

Summary

This summary is machine-generated.

Publication bias and p-hacking undermine scientific literature and meta-analyses. This study proposes a mixture model to address p-hacking, improving the reliability of research findings.

Keywords:

file drawer problem fishing for significance meta-analysis questionable research practices selection bias

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Area of Science:

Statistics
Biostatistics
Scientific Methodology

Background:

Publication bias and p-hacking significantly impact scientific literature integrity.
These biases violate meta-analysis assumptions, compromising study result reliability.
Existing methods effectively address publication bias but struggle with p-hacking.

Purpose of the Study:

To advocate for the selection model approach in addressing publication bias.
To propose a novel mixture model specifically designed for p-hacking.
To evaluate the performance of these models through theoretical derivations and simulations.

Main Methods:

Utilizing the selection model framework to capture publication bias.
Developing and implementing a mixture model to account for p-hacking.
Conducting formal derivations and simulation studies for model comparison.
Applying the proposed models to real-world data examples.

Main Results:

The selection model effectively addresses publication bias.
The proposed mixture model offers a viable approach for modeling p-hacking.
Simulations and real data analyses demonstrate the practical utility of the models.

Conclusions:

The developed models enhance the accuracy and trustworthiness of meta-analyses.
This work provides a robust framework for handling publication bias and p-hacking.
The proposed methods offer practical solutions for improving scientific literature quality.