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Related Concept Videos

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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Positive, Negative, and Zero Work00:58

Positive, Negative, and Zero Work

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Work is done on an object when energy is transferred to the object. In other words, work is done when a force acts on a body that undergoes a displacement from one position to another. By definition, the work done by a force is the integral of the force with respect to the displacement along its path. Forces can vary as a function of position, and displacements can occur along various paths between two points. The magnitude of a force multiplied by the cosine of the angle that the force makes...
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Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Archival Research01:40

Archival Research

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Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
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Data: Types and Distribution01:19

Data: Types and Distribution

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In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
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Negative Regulator Molecules01:23

Negative Regulator Molecules

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Positive regulators allow a cell to advance through cell cycle checkpoints. Negative regulators have an equally important role as they terminate a cell’s progression through the cell cycle—or pause it—until the cell meets specific criteria.
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Related Experiment Video

Updated: Jan 1, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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Published on: November 22, 2019

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Why (and how) we should publish negative data.

Simon Nimpf1, David A Keays1

  • 1Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria.

EMBO Reports
|December 21, 2019
PubMed
Summary

Publishing negative scientific results requires robust evidence and clear arguments to overcome editorial and reviewer skepticism. This ensures the integrity and progress of the scientific method.

Area of Science:

  • Scientific methodology
  • Research integrity

Background:

  • Negative results are essential for scientific progress.
  • Publication bias often hinders the dissemination of negative findings.

Purpose of the Study:

  • To outline strategies for effectively communicating negative results.
  • To provide guidance for authors seeking to publish refutations and null findings.

Main Methods:

  • Analysis of editorial and reviewer feedback on negative result submissions.
  • Development of a framework for presenting negative data persuasively.

Main Results:

  • Key elements for successful publication include rigorous methodology and clear interpretation.
  • Strong counterarguments and alternative explanations are vital for refutations.

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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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Conclusions:

  • Overcoming publication bias requires a concerted effort from researchers, editors, and reviewers.
  • Strengthening the presentation of negative results enhances scientific transparency and efficiency.