Famous Statistics Quotes in English

Here’s a list of famous statistics quotes with meanings and examples to explain their significance:

1. “There are three kinds of lies: lies, damned lies, and statistics.” – Mark Twain (attributed)

Meaning: This quote suggests that statistics can be manipulated or presented in misleading ways, sometimes to support false claims.

Example: A company advertises a product claiming "90% of people prefer it." However, the survey was conducted on a very small and biased sample. Here, statistics are used to present a misleading conclusion, illustrating how numbers can sometimes obscure the truth.

2. “Statistics are like bikinis. What they reveal is suggestive, but what they conceal is vital.” – Aaron Levenstein

Meaning: This quote humorously points out that statistics often provide a partial view, and the most crucial information might be hidden beneath the surface.

Example: A report shows that a city’s average income is high, but it conceals income disparity: most people earn much less, and a few wealthy individuals raise the average. The statistic suggests prosperity, but the hidden inequality is the crucial part of the story.

3. “Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.” – H.G. Wells

Meaning: Wells emphasizes the importance of understanding statistics in making informed decisions as a citizen in modern society.

Example: During an election, citizens need to interpret poll results and demographic data critically. Understanding basic statistics helps them assess which polls may be biased and which might give a realistic forecast of the election outcome.

4. “Statistics: the only science that enables different experts using the same figures to draw different conclusions.” – Evan Esar

Meaning: Esar humorously points out that interpretations of data can vary widely, even when the same data is used, depending on biases or analytical methods.

Example: Two economists interpret employment data: one focuses on job growth as a positive indicator, while the other highlights underemployment as a warning. Both are using the same statistics, but their conclusions differ based on which aspects they emphasize.

5. “Without data, you're just another person with an opinion.” – W. Edwards Deming

Meaning: Deming emphasizes that data provides a foundation for informed decision-making, distinguishing facts from mere opinions.

Example: In a debate on healthcare, one participant cites specific data on patient outcomes, while another only shares personal beliefs. The data-backed argument has more credibility, illustrating how statistics can elevate discussions from opinion to evidence-based conclusions.

6. “In God we trust. All others must bring data.” – W. Edwards Deming

Meaning: This quote humorously conveys that in practical matters, opinions and beliefs are less reliable than hard data.

Example: A manager proposes a new business strategy based on intuition. The CEO insists on seeing supporting data, such as previous case studies and market research, to evaluate the strategy’s feasibility before committing resources.

7. “Statistics may be defined as 'a body of methods for making wise decisions in the face of uncertainty.’” – W.A. Wallis

Meaning: Wallis emphasizes the role of statistics in helping people make informed choices when outcomes are uncertain.

Example: In medicine, doctors use statistics on treatment success rates and patient demographics to choose the most effective treatments. These decisions are based on probability and data, helping reduce uncertainty in treatment outcomes.

8. “The average is one of the most useful and dangerous concepts in statistics.” – H.G. Wells

Meaning: Wells warns that while averages summarize data, they can also misrepresent it by masking variability or extremes.

Example: The average salary in a company may seem high, but it might hide that some employees earn significantly less than the average, with a few top executives making disproportionately high incomes. This example shows how averages can distort the true distribution of data.

9. “An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem.” – John Tukey

Meaning: Tukey suggests that it’s better to address the right questions with rough accuracy than to pursue the wrong questions with perfect precision.

Example: A social scientist focuses on approximate but meaningful metrics of quality of life, rather than overly precise but less relevant data. This allows them to draw insights that better capture the community’s reality.

10. “Statistics is the grammar of science.” – Karl Pearson

Meaning: Pearson argues that statistics is essential to scientific inquiry, as it provides the structure and tools needed to interpret data objectively.

Example: In a study on climate change, researchers use statistical analysis to process temperature data over decades. Without statistics, the data would be a raw list of numbers; with it, they can analyze trends and draw conclusions on climate patterns.

11. “You can have data without information, but you cannot have information without data.” – Daniel Keys Moran

Meaning: Moran highlights that data alone is not useful until it is organized and interpreted into meaningful information.

Example: A company collects massive amounts of data on customer behavior, but until they analyze it, they cannot derive actionable insights. Only after analyzing patterns and trends does the data become valuable information for business decisions.

12. “There is a magic in graphs. The profile of a curve reveals in a flash a whole situation.” – Henri Poincaré

Meaning: Poincaré emphasizes that visual representation, like graphs, can make complex data instantly understandable.

Example: In tracking COVID-19 cases, a line graph showing the curve of infection rates quickly reveals spikes, plateaus, or declines, making it easier for the public and policymakers to grasp the situation at a glance.

13. “Statistics is the art of never having to say you’re wrong.” – Anonymous

Meaning: This humorous quote highlights the adaptability of statistics, suggesting that with the right interpretation, data can be made to support nearly any argument.

Example: A sports analyst predicts a team’s victory using certain statistics. When the team loses, they reinterpret the data to suggest the result was an "outlier." The adaptability of statistics allows them to avoid admitting they were incorrect.

14. “All models are wrong, but some are useful.” – George Box

Meaning: Box points out that statistical models are simplifications and inherently imperfect, but they can still be valuable tools for understanding complex realities.

Example: A financial model might not perfectly predict stock market behavior due to numerous unpredictable factors, but it can still provide valuable insights on market trends, helping investors make informed decisions.

15. “Correlation does not imply causation.” – Common Statistical Principle

Meaning: This principle warns that just because two variables are related, one does not necessarily cause the other.

Example: A study finds a correlation between ice cream sales and drowning incidents. This does not mean ice cream causes drowning; rather, both are likely associated with hot weather, which increases swimming activity and ice cream consumption.

These quotes highlight the role of statistics in shaping our understanding, decision-making, and insights. They also reveal the nuances and potential pitfalls that come with interpreting data.