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What is the use of statistics in science?

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Amelie Amira
What is the use of statistics in science?

This is a good question. I would say that essentially every present-day science uses statistics homework help techniques as a key process in exploratory research and theory confirmation, to a greater or lesser extent (usually greater).

Sometimes these are traditional statistical methods, other times they are newer “data science” techniques such as machine learning (most of these algorithms draw upon statistical and probabilistic concepts).


As a general statement, pretty well all lab sciences depend on statistical methods for error analysis. The ideas behind hypothesis testing and confidence intervals are also ubiquitous in physical, medical and social sciences.

Here are some more specific examples, though far from exhaustive.



  • Physics: If you take a physics degree, you are likely to come to a course called “Statistical Physics” or words to that effect. So, that tells you something right there.
  • A lot of thermodynamics has a heavy statistical focus (e.g. temperature is explained as the average kinetic energy of a large ensemble of molecules).
  • Geophysics leans very heavily on statistical theory, especially time series analysis (e.g. I took a course called “Time Series Analysis in Geophysics”). Very useful in exploration geophysics and earthquake analysis.
  • Astrophysics makes extensive use of statistics in analysis of stellar spectra (e.g. Power spectrum analysis helps to find planets in other solar systems). A lot of error analysis was developed for astronomical observations in earlier centuries.
  • Particle physics makes use of statistical techniques to establish whether a new particle has actually been discovered (e.g. you hear things like “it is a 3 sigma event” when reports from particle accelerators are discussed).


  • Geology and Earth Sciences: This makes use of statistics for many reasons:
  • Geophysical methods as outlined above.
  • Methods for estimating ore reserves or petroleum deposits depend heavily on sampling theory.


  • Biological SciencesLots of statistical techniques used here as well:
  • Population genetics makes use of many statistical methods, such as cluster analysis to make taxonomical decisions.
  • Molecular genetics (e.g. the big GWAS studies about human evolution) uses statistical methods, such as regression (whether via statistical methods or machine learning methods).
  • A lot of statistical theory goes back to agricultural studies.


  • Computing Science:
  • The newer machine learning methods often make extensive use of statistical theory:
  • Computer network design makes use of principles from probability theory for matter such as queuing algorithms.
  • Monte Carlo methods are largely based on statistical theory.


  • Psychology and Other Quantitative Social Sciences:
  • These disciplines make extensive use of statistical methods, such as regression, ANOVA, factor analysis and cluster analysis.
  • Demography is an important sub-discipline of sociology, which is very statistical in nature.


  • Economics:
  • Economics makes extensive use of various types of regression analysis (e.g. OLS, logit, time series) and other very complex methods.
  • Marketing is very heavily dependent on statistical analysis. In fact, many statistical methods came about for marketing purposes (e.g. A/B studies).


  • Medicine:
  • Makes very extensive use of statistical theory to determine the efficacy of new drugs and treatments.
  • Meta-analysis is widely used to do “studies of studies”.
  • Epidemiology is essentially statistical in nature (e.g. the famous study of how typhus was spread via water pumps was essentially a correlation study).


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Amelie Amira
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