Topic
Observer-expectancy effect
About: Observer-expectancy effect is a research topic. Over the lifetime, 46 publications have been published within this topic receiving 4175 citations. The topic is also known as: Experimenter's bias & observer bias.
Papers
TL;DR: Readers of medical literature need to consider two types of validity, internal and external: external validity is the ability to generalise from the study to the reader's patients, and internal validity means that the study measured what it set out to.
1,508 citations
TL;DR: The major common challenges and flaws that emerge in using and interpreting statistical tests of heterogeneity and bias in meta-analyses are discussed and suggestions are made on how to avoid these flaws, use these tests properly and learn from them.
Abstract: Statistical tests of heterogeneity and bias, in particular publication bias, are very popular in meta-analyses. These tests use statistical approaches whose limitations are often not recognized. Moreover, it is often implied with inappropriate confidence that these tests can provide reliable answers to questions that in essence are not of statistical nature. Statistical heterogeneity is only a correlate of clinical and pragmatic heterogeneity and the correlation may sometimes be weak. Similarly, statistical signals may hint to bias, but seen in isolation they cannot fully prove or disprove bias in general, let alone specific causes of bias, such as publication bias in particular. Both false-positive and false-negative signals of heterogeneity and bias can be common and their prevalence may be anticipated based on some rational considerations. Here I discuss the major common challenges and flaws that emerge in using and interpreting statistical tests of heterogeneity and bias in meta-analyses. I discuss misinterpretations that can occur at the level of statistical inference, clinical/pragmatic inference and specific cause attribution. Suggestions are made on how to avoid these flaws, use these tests properly and learn from them.
602 citations
TL;DR: People's tendency to deny their own bias, even while recognizing bias in others, reveals a profound shortcoming in self-awareness, with important consequences for interpersonal and intergroup conflict.
439 citations
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TL;DR: Basic issues related to bias in research are described, which include the need to be aware of all potential sources of bias, and undertake all possible actions to reduce or minimize the deviation from the truth.
Abstract: By writing scientific articles we communicate science among colleagues and peers. By doing this, it is our responsibility to adhere to some basic principles like transparency and accuracy. Authors, journal editors and reviewers need to be concerned about the quality of the work submitted for publication and ensure that only studies which have been designed, conducted and reported in a transparent way, honestly and without any deviation from the truth get to be published. Any such trend or deviation from the truth in data collection, analysis, interpretation and publication is called bias. Bias in research can occur either intentionally or unintentionally. Bias causes false conclusions and is potentially misleading. Therefore, it is immoral and unethical to conduct biased research. Every scientist should thus be aware of all potential sources of bias and undertake all possible actions to reduce or minimize the deviation from the truth. This article describes some basic issues related to bias in research.
432 citations
TL;DR: The authors found that the bias is largely based on the distinction between direct and indirect causation, rather than that between action and inaction as such, and argued that concern about omission bias is justified if only a substantial minority of people show it.
391 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2019 | 1 |
| 2017 | 3 |
| 2016 | 3 |
| 2015 | 3 |
| 2014 | 4 |
| 2013 | 5 |