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Related Concept Videos

Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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...
Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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 Guinness...
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...

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Assessment and Communication for People with Disorders of Consciousness
07:37

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Published on: August 1, 2017

Confidence interval for statistical power using a sample variance estimate.

Xiaofeng Liu1

  • 1University of South Carolina, Department of Educational Studies, Columbia, USA. xliu@mailbox.sc.edu

Nurse Researcher
|September 6, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a confidence interval for statistical power estimation, improving accuracy by accounting for variance uncertainty. This method offers a more realistic assessment of power for planned studies.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methodology

Background:

  • Statistical power estimation often relies on sample variance from prior studies, leading to inaccurate power values.
  • Using sample variance introduces uncertainty, potentially overestimating the actual statistical power in planned research.

Purpose of the Study:

  • To enhance the precision of statistical power estimation by developing a method to construct its confidence interval.
  • To provide a more realistic assessment of statistical power in clinical studies.

Main Methods:

  • A methodology paper detailing the construction of a confidence interval for statistical power.
  • Utilizes standard deviation for body mass index estimated from weight-loss research literature.

Main Results:

  • The confidence interval offers a more accurate representation of statistical power compared to single-value estimates.
  • Addresses the uncertainty introduced by using estimated variance in power analysis.

Conclusions:

  • Recommends using confidence intervals for statistical power analysis to account for variance estimation uncertainty.
  • The proposed technique for constructing a confidence interval on statistical power is practical for planning clinical studies and sample sizes.