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Related Concept Videos

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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Arrhenius Plots02:34

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The Arrhenius equation relates the activation energy and the rate constant, k, for chemical reactions. In the Arrhenius equation, k = Ae−Ea/RT, R is the ideal gas constant, which has a value of 8.314 J/mol·K, T is the temperature on the kelvin scale, Ea is the activation energy in J/mole, e is the constant 2.7183, and A is a constant called the frequency factor, which is related to the frequency of collisions and the orientation of the reacting molecules.
The Arrhenius equation can be used...
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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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Hypothesis: Accept or Fail to Reject?01:17

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The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
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pV-Diagrams01:18

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The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...
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Residual Plots01:07

Residual Plots

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A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
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The P value plot does not provide evidence against air pollution hazards.

Daniel J Hicks1

  • 1University of California, Merced, Merced, California.

Environmental Epidemiology (Philadelphia, Pa.)
|April 18, 2022
PubMed
Summary

The P value plot method, used to critique air pollution studies, was investigated. Simulations show it does not reliably detect heterogeneity or P hacking, and its zero-effect findings are not supported by evidence.

Keywords:
Air pollutionP hackingP value plotSimulation methods

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Area of Science:

  • Environmental Health
  • Biostatistics
  • Philosophy of Science

Background:

  • Epidemiological studies and meta-analyses on air pollution hazards have faced criticism using a graphical method termed the P value plot.
  • This method, developed by Young and collaborators, claims to identify zero effects, heterogeneity, and P hacking.
  • The P value plot method lacks validation in peer-reviewed literature.

Purpose of the Study:

  • To investigate the statistical and evidentiary properties of the P value plot method.
  • To assess the validity of claims made using the P value plot regarding air pollution research.

Main Methods:

  • A simulation was developed to generate studies and meta-analyses with known true effects.
  • The simulation integrated two quantifiable concepts of evidence from philosophy of science.
  • The simulation code and analysis are publicly available and reproducible.

Main Results:

  • The P value plot did not provide evidence for heterogeneity or P hacking under simulated conditions.
  • The plot can indicate zero effects, but only when specific, unmet conditions are present.
  • Actual applications of the P value plot by Young and collaborators did not meet these conditions.

Conclusions:

  • The P value plot method fails to provide robust evidence for the skeptical claims made by Young and collaborators regarding air pollution hazards.
  • The statistical and evidentiary basis of the P value plot is questionable.