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The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
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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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Chemistry is an empirical science. Scientists often pose questions to understand the chemistry in everyday life and seek answers to these questions. To achieve this, scientists follow a definitive series of steps that together make up the Scientific Method. This approach involves making observations, asking questions, building a hypothesis, conducting experiments, analyzing results, and forming a conclusion. 
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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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Updated: Dec 29, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
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What's so special about empirical adequacy?

Sindhuja Bhakthavatsalam1, Nancy Cartwright2,3

  • 11Liberal Studies Program, California State University, Northridge, Northridge, CA USA.

European Journal for Philosophy of Science
|February 4, 2020
PubMed
Summary

Empirical adequacy is not essential for scientific theories to be useful. Theories can effectively aid understanding and managing the world, even if not perfectly matching all observable facts.

Keywords:
Aims of scienceAntirealismEmpirical adequacyPragmatismScientific realismScientific truthScientific understanding

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Area of Science:

  • Philosophy of Science
  • Theoretical Frameworks

Background:

  • Scientific theories are often evaluated based on empirical adequacy, which is the ability to accurately predict observable phenomena.
  • Both scientific realists and antirealists consider empirical adequacy a key criterion for theory acceptance.

Purpose of the Study:

  • To challenge the primacy of empirical adequacy in evaluating scientific theories.
  • To propose alternative criteria for theory evaluation: understanding and managing the world.
  • To investigate the role of empirical adequacy in achieving these alternative goals.

Main Methods:

  • Conceptual analysis of the roles of scientific theories.
  • Philosophical argumentation regarding the utility of empirical adequacy.
  • Exploration of how theories contribute to understanding and managing the world.

Main Results:

  • Empirical adequacy is not a necessary condition for a theory to facilitate understanding.
  • Empirical adequacy can hinder efficient world management and understanding.
  • Understanding and managing the world are often more important functions of theories than empirical adequacy.

Conclusions:

  • The traditional emphasis on empirical adequacy in science may be misplaced.
  • Alternative criteria, such as explanatory power and practical utility, should be prioritized.
  • Rethinking theory evaluation can lead to more effective scientific progress.