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Significance unfortunately is a useful means toward a…

“Significance unfortunately is a useful means toward a personal ends in the advance of science - status and widely distributed publications, a big laboratory, a staff of research assistants, a reduction in teaching load, a better salary, the finer wines of Bordeaux. Precision, knowledge, and…” quote by Stephen Thomas Ziliak
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““Significance unfortunately is a useful means toward a personal ends in the advance of science - status and widely distributed publications, a big laboratory, a staff of research assistants, a reduction in teaching load, a better salary, the finer wines of Bordeaux. Precision, knowledge, and control. In a narrow and cynical sense statistical significance is the way to achieve these. Design experiment. Then calculate statistical significance. Publish articles showing "significant" results. Enjoy promotion. But it is not science, and it will not last.””

Stephen Thomas Ziliak

About This Quote

This interpretation was drafted with AI assistance. It is one reading of the quote, not the author's own explanation.

Statistical significance is often misused as a shortcut to career advancement rather than genuine scientific discovery, making it a fragile foundation for true research.

In simple terms: Statistical significance is used for personal gain, not true science.

Key Takeaway

Prioritize rigorous methodology over metric chasing.

Themes

science research statistics career ethics

Mood

critical analytical concerned

Type

Education:0 Science:0 Significance:1 Statistical-analysis:1 Statistics:1 Statistical Significance:1 Significance Unfortunately:0 Significance Publish:0 Significance Way:0

When to use this quote

  • academic publishing
  • grant writing
  • research design
  • career planning

Key Concepts

philosophy of science methodology economics

Questions to Reflect On

  • How can institutions reward genuine discovery?
  • What alternatives to significance metrics exist?
A Different Perspective

The quote may overgeneralize; many researchers value integrity over metrics.

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