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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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Estimating Population Mean with Unknown Standard Deviation01:22

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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Estimating Population Standard Deviation01:26

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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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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Contaminants and Errors

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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
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Sample size calculations for indirect standardization.

Yifei Wang1, Philip Chu2

  • 1Department of Radiology, Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, USA. yifei.wang@ucsf.edu.

BMC Medical Research Methodology
|April 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces new statistical methods for sample size calculation in indirect standardization. These methods do not require knowing the index hospital's covariate distribution, improving hospital outcome comparisons.

Keywords:
Hospital profilingIndirect standardizationSample size calculation

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Area of Science:

  • Health Services Research
  • Biostatistics
  • Epidemiology

Background:

  • Indirect standardization and standardized incidence ratios (SIR) are vital for hospital profiling.
  • Traditional SIR methods assume known covariate distributions, limiting sample size calculations.
  • Accurate sample size is crucial for high-powered statistical inference in healthcare quality assessment.

Purpose of the Study:

  • To develop novel statistical methodology for sample size calculation in indirect standardization.
  • To enable sample size determination without prior knowledge of the index hospital's covariate distribution.
  • To address limitations in existing methods for hospital outcome comparisons.

Main Methods:

  • Development of new statistical techniques for sample size calculation.
  • Methodology bypasses the need to know or estimate index hospital covariate distributions.
  • Application of methods to simulation studies and real-world hospital data.

Main Results:

  • The proposed methodology successfully performs sample size calculations for SIR without index hospital covariate distributions.
  • Evaluated performance in simulations and real hospital data demonstrated the method's utility.
  • Comparison with traditional methods highlights the advantages of the novel approach.

Conclusions:

  • Novel methods provide a robust approach to sample size calculation for indirect standardization.
  • This advancement enhances the ability to conduct high-powered studies in hospital profiling.
  • The findings are applicable to improving the reliability of comparative hospital outcome research.