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Related Concept Videos

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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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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Statistical Significance01:37

Statistical Significance

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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Unusual Results01:16

Unusual Results

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Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value =...
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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P values: from suggestion to superstition.

John Concato1, John A Hartigan2

  • 1Clinical Epidemiology Research Center, Cooperative Studies Program, Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.

Journal of Investigative Medicine : the Official Publication of the American Federation for Clinical Research
|August 5, 2016
PubMed
Summary
This summary is machine-generated.

The common p≤0.05 threshold for statistical significance in clinical research is often misused. A comprehensive evaluation of evidence, including p-values and confidence intervals, is crucial for accurate interpretation.

Keywords:
BiostatisticsClinical ResearchResearch Design

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Area of Science:

  • Clinical Research Methodology
  • Biostatistics
  • Genomic Data Analysis

Background:

  • The p-value threshold of p≤0.05 is a widely adopted standard for determining statistical significance in clinical studies.
  • Historically, this threshold was a suggestion, not a rigid rule, acknowledging a 1-in-20 chance of a false-positive result.
  • Current interpretations often misapply p≤0.05 as a definitive 'win' or 'lose' criterion, overlooking its probabilistic nature.

Purpose of the Study:

  • To define the p-value and trace its historical origins in hypothesis testing.
  • To examine the application and common misunderstandings of the p≤0.05 threshold in clinical research.
  • To discuss emerging significance thresholds in genomic studies and advocate for a nuanced interpretation of statistical results.

Main Methods:

  • Literature review of the historical development and application of p-values in statistical hypothesis testing.
  • Analysis of the conceptual underpinnings of null hypothesis significance testing.
  • Discussion of contemporary genomic analysis standards and their implications for clinical research.

Main Results:

  • The p≤0.05 threshold, initially a suggestion, is frequently misinterpreted as an absolute determinant of study validity.
  • Genomic studies increasingly utilize stringent thresholds like p≤10⁻⁸ due to the high number of comparisons performed.
  • Over-reliance on a single p-value threshold can lead to flawed conclusions regarding exposure-outcome associations.

Conclusions:

  • Statistical significance thresholds, including p≤0.05, should not be applied reflexively to judge the trustworthiness of clinical research.
  • Interpreting quantitative results requires a holistic approach, considering the strength of association, p-values, confidence intervals, and sample size.
  • A balanced assessment is essential for valid and generalizable conclusions in clinical and genomic research.