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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

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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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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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Statistical Significance01:50

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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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Significance Testing: Overview01:04

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Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
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Regression Toward the Mean01:52

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. On The Effect Of Flexible Adjustment Of The P Value Significance Threshold On The Reproducibility Of Randomized Clinical Trials.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. On The Effect Of Flexible Adjustment Of The P Value Significance Threshold On The Reproducibility Of Randomized Clinical Trials.

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On the effect of flexible adjustment of the p value significance threshold on the reproducibility of randomized clinical trials.

Farrokh Habibzadeh1

  • 1Independent Research Consultant, Shiraz, Iran.

Plos One
|June 13, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

The study suggests a flexible p-value threshold can mitigate the reproducibility crisis by reducing errors. However, this approach conflicts with frequentist methods, indicating a need for alternative statistical analyses like Bayesian methods.

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Area of Science:

  • Statistical inference
  • Reproducibility in research
  • Clinical trial methodology

Background:

  • The scientific community faces a reproducibility crisis, partly attributed to the arbitrary 0.05 p-value threshold.
  • Lowering the threshold to 0.005 reduces false positives but increases false negatives.
  • A novel flexible p-value threshold aims to minimize errors in hypothesis testing.

Purpose of the Study:

  • To compare error rates (false-positive and false-negative) using fixed (0.05, 0.005) and flexible p-value thresholds.
  • To evaluate the impact of sample size on error rates under different threshold conditions.
  • To assess the flexible threshold's effectiveness in addressing the reproducibility crisis.

Main Methods:

  • In silico study using Monte Carlo simulations.
  • Calculation of false-positive and false-negative rates.
  • Comparison across three p-value threshold conditions: 0.05, 0.005, and a flexible threshold.

    • Main Results:

    • Flexible threshold use significantly reduced the false-positive rate compared to fixed thresholds.
    • Increasing sample size decreased false-negative rates but did not affect fixed false-positive rates.
    • The flexible threshold, while improving reproducibility, revealed conflicts within the frequentist framework, as it's calculated post-hoc.

    Conclusions:

    • Frequentist inference and p-values may be insufficient for reliable scientific conclusions.
    • Alternative statistical approaches, such as Bayesian methods, should be prioritized for data analysis.
    • The limitations of the flexible threshold highlight the need for re-evaluating current statistical practices.