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Related Concept Videos

Confidence Intervals01:21

Confidence Intervals

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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.
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Interpretation of Confidence Intervals01:19

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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.
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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...
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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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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...
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P-value01:10

P-value

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P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more...
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Alternatives to P value: confidence interval and effect size.

Dong Kyu Lee1

  • 1Department of Anesthesiology and Pain Medicine, Guro Hospital, Korea University School of Medicine, Seoul, Korea.

Korean Journal of Anesthesiology
|December 8, 2016
PubMed
Summary

Null hypothesis significance testing (NHST) and p-values are criticized for misinterpreting data. Effect sizes and confidence intervals (CIs) offer a better approach to understanding treatment effects in research.

Keywords:
Confidence intervalsEffect sizesP value

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Area of Science:

  • Statistics in Anesthesiology
  • Biostatistics
  • Research Methodology

Background:

  • Null hypothesis significance testing (NHST) and p-values are central to statistical analysis but face significant criticism.
  • NHST's dichotomous classification of treatments (significant vs. non-significant) limits practical interpretation.
  • Previous articles in the Korean Journal of Anesthesiology have highlighted concerns regarding NHST.

Approach:

  • This article introduces effect sizes and confidence intervals (CIs) as complementary statistical measures.
  • It explains the fundamental concepts and estimation principles of effect sizes and CIs.
  • The aim is to expand statistical thinking beyond traditional NHST.

Key Points:

  • Effect sizes quantify the magnitude of treatment effects, providing practical significance.
  • Confidence intervals (CIs) offer a range of plausible values for the true effect size.
  • Both measures enhance the interpretation of research findings, aiding authors and readers.
  • NHST's limitations in assessing practical importance are addressed by these alternative approaches.

Conclusions:

  • Effect sizes and CIs provide a more comprehensive understanding of treatment effects than NHST alone.
  • Adopting these measures can lead to more nuanced and informative statistical interpretations in scientific literature.
  • This approach supports better discrimination between various treatment effects, improving research quality.