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The precipitation titration curve demonstrates the change in concentration of one reactant with the volume of titrant added. During the titration of chloride ions with silver nitrate, the precipitation titration curve is divided into three regions: before, at, and after the equivalence point. Before the equivalence point, low redissolution of the sparingly soluble silver chloride precipitate gives a low silver ion concentration. However, in the second region, representing the equivalence point,...
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When a substance—isolated from its environment—is subjected to heat changes, corresponding changes in temperature and phase of the substance is observed; this is graphically represented by heating and cooling curves.
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A titration curve is a plot of some solution property versus the amount of added titrant. For acid-base titrations, solution pH is a useful property to monitor because it varies predictably with the solution composition and, therefore, may be used to monitor the titration’s progress and detect its endpoint. Acid-base titration can be performed with a strong acid and a strong base, a strong acid and a weak base, or a strong base and a weak acid.
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Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
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Related Experiment Video

Updated: Feb 16, 2026

Recapitulation of an Ion Channel IV Curve Using Frequency Components
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What does a Z-curve analysis tell us?

Jolynn Pek1, Hao Wu2, Yang Liu3

  • 1Department of Psychology, The Ohio State University, Columbus, OH, USA.

Cognition & Emotion
|February 14, 2026
PubMed
Summary
This summary is machine-generated.

Z-curve analysis, intended to assess research credibility, is often misunderstood. Its Expected Discovery Rate (EDR) is not a reliable indicator of credibility, and the method itself can yield biased results, making it unsuitable for evaluating findings.

Keywords:
Z-curveaverage powercredibilityexpected discovery rateforensic meta-analysis

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Area of Science:

  • Psychological science
  • Research methodology
  • Statistical inference

Background:

  • Z-curve analysis is a statistical tool proposed to assess the credibility of research findings.
  • However, its interpretation and statistical underpinnings are frequently misunderstood.
  • The Expected Discovery Rate (EDR), a key metric in Z-curve analysis, is conceptually distinct from pre-data power and its link to credibility is unclear.

Purpose of the Study:

  • To clarify the statistical properties and interpretation of Z-curve analysis.
  • To evaluate the reliability of Z-curve applications in published research.
  • To assess the validity of claims regarding research credibility based on Z-curve analysis, particularly in emotion science.

Main Methods:

  • Review of 37 articles that reported 278 Z-curve applications.
  • Analysis of methodological issues in reported Z-curve studies, including p-value independence and focal finding status.
  • Computer simulations to assess the bias and consistency of Z-curve estimators.

Main Results:

  • A significant majority (77.3%) of reviewed Z-curve applications concluded publication bias.
  • However, many studies had methodological shortcomings: 48.2% did not specify if p-values were for focal findings, and 69.1% may have violated the independence assumption.
  • Simulations indicated that Z-curve estimators can be biased, inconsistent, and may not adhere to the Law of Large Numbers, leading to potentially misleading conclusions.

Conclusions:

  • Z-curve analysis is not recommended for evaluating research findings due to misunderstood interpretation and statistical limitations.
  • The Expected Discovery Rate (EDR) is not a reliable measure of credibility.
  • Traditional meta-analytic methods are more appropriate and reliable for drawing statistical conclusions about research findings.