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Bias01:22

Bias

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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...
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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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:  
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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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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%...
369
Chi-square Analysis02:46

Chi-square Analysis

40.0K
The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
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Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
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Reporting all results efficiently: A RARE proposal to open up the file drawer.

David D Laitin1,2, Edward Miguel3,4,5, Ala' Alrababa'h6,7

  • 1Department of Political Science, Stanford University, Stanford, CA 94305; dlaitin@stanford.edu.

Proceedings of the National Academy of Sciences of the United States of America
|December 22, 2021
PubMed
Summary

Publication bias persists in social sciences despite progress in transparency. A new framework, Report All Results Efficiently (RARE), incentivizes full reporting to improve research reliability and reduce wasted resources.

Keywords:
file drawer problemnull findingspublication biasregistriesresearch transparency

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Area of Science:

  • Social Sciences
  • Empirical Research
  • Publication Bias

Background:

  • Social sciences increasingly adopt transparent research practices for verification and replication.
  • Publication bias remains a significant issue, skewing literature towards statistically significant findings, potentially including false positives.

Purpose of the Study:

  • To propose a framework, Report All Results Efficiently (RARE), to mitigate publication bias in social sciences.
  • To incentivize the efficient reporting of all research results, thereby enhancing confidence in findings.

Main Methods:

  • Focuses on experimental and prospective empirical social science research utilizing public study registries.
  • Proposes an integrated system leveraging public registries, IRBs, journals, and funding agencies.
  • Aims to incentivize full reporting through existing infrastructure.

Main Results:

  • The RARE framework is designed to enable efficient reporting of all study outcomes.
  • Implementation can improve confidence in social science findings by reducing bias.
  • A coordinated research ecosystem prevents redundant investigations and reduces funding waste.

Conclusions:

  • The RARE framework offers a systematic approach to combat publication bias in social sciences.
  • Enhancing research transparency through full result reporting strengthens the scientific record.
  • This initiative promotes efficient use of research funding and investigator time.