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Related Concept Videos

Standard Error of the Mean01:13

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The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.
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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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William S. Gosset (1876–1937) of the...
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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Every measurement provides three kinds of information: the size or magnitude of the measurement (a number), a standard of comparison for the measurement (a unit), and an indication of the uncertainty of the measurement. While the number and unit are explicitly represented when a quantity is written, the uncertainty is an aspect of the errors in the measurement results.
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Addressing the estimation of standard errors in fixed effects meta-analysis.

Clara Domínguez Islas1,2, Kenneth M Rice3

  • 1Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Statistics in Medicine
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PubMed
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Standard meta-analysis methods incorrectly assume known standard errors. This study reveals how using estimated standard errors, even in large samples, miscalibrates inference and impacts confidence intervals in fixed effects meta-analysis.

Keywords:
fixed effectsheterogeneitymeta-analysisrandom effects

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Area of Science:

  • Biostatistics
  • Statistical Inference
  • Medical Research Methodology

Background:

  • Standard fixed effects meta-analysis assumes known study-specific standard errors.
  • The impact of using estimated standard errors is not fully understood.
  • Existing methods lack tools for realistic inference.

Purpose of the Study:

  • To elucidate the impact of estimated standard errors in fixed effects meta-analysis.
  • To demonstrate why this impact persists in large samples.
  • To quantify the miscalibration of standard inference when estimated standard errors are ignored.

Main Methods:

  • Analysis of fixed effects meta-analysis under estimated standard errors.
  • Investigation of the role of heterogeneity measures in miscalibration.
  • Development of improved confidence intervals.

Main Results:

  • The assumption of known standard errors leads to miscalibrated inference.
  • This miscalibration persists even with large sample sizes.
  • A specific measure of heterogeneity plays a key role in the miscalibration.

Conclusions:

  • Ignoring estimated standard errors in fixed effects meta-analysis causes significant inferential errors.
  • Improved confidence intervals are developed for better location and scale parameter estimation.
  • This research provides tools for more accurate meta-analysis under realistic assumptions.