Statistics: Collecting Data
Chapter 3 – Statistics: Collecting Data
Introduction to Collecting Data
Importance of Taking Control of Your Life
Ability to evaluate daily data and claims is essential
Distinguishing good from faulty reasoning is crucial to avoid manipulation
Statistics provide tools necessary for intelligent reactions to information
Statistics is considered one of the most important subjects to study
Claims and Examples
Various claims that illustrate the relevance of statistics:
4 out of 5 dentists recommend Dentyne
Tobacco-related lung cancers: 85% in men; 45% in women
Condom effectiveness: 94% effectiveness rate
Street crossing accidents among Native Americans: significantly more likely to be hit
Persuasiveness: Increased when speakers maintain direct eye contact and speak loudly and quickly
Gender wage gap: Women earn 75 cents for every dollar earned by men for the same job
Egg whites and lifespan: A surprising study suggesting a positive correlation
Baseball batting average: Low probability of another player exceeding .400 average
Birthday paradox: 80% chance that in a room of 30, at least two people share the same birthday
COVID-19 Vaccine: Pfizer-BioNTech vaccine was 95% effective based on clinical trials
Joke statistic: 79.48% of all statistics are made up on the spot
Statistical Claims Across Various Domains
Claims represent diverse subjects—psychology, health, law, sports, business, etc.
Statistics are often used to add credibility to arguments or advice
Be cautious: many presented statistics are not from careful analysis and can be misleading
The Importance of Learning Statistics
Learning about statistics is essential for informed decision-making and autonomy
While statistics help guard against manipulation, they also aid in recognizing valid claims
Understanding statistics involves questioning the sources, numbers, and procedures behind claims
Examples Highlighting Statistical Misinterpretation
Advertisement Effectiveness Case:
A claim states a 30% increase in ice cream sales due to a new advertisement
Flaw: Ice cream sales generally rise in warmer months (historical effect)
Misinterpretation of causal relationships due to a third variable (time of year)
Churches and Crime Rates:
Claim: More churches lead to more crime
Flaw: Both are correlated to city population growth
Misleading causal inference due to the third-variable problem (population)
Interracial Marriages Statistics:
Claim: 75% increase in interracial marriages indicates societal acceptance
Flaw: Lacks historical context; the sheer percentage does not indicate the overall rate
Insufficient data to support robust conclusions about societal trends
The Value of Critical Thinking
Important to begin questioning the statistics encountered daily
Quotation from Benjamin Disraeli: “There are three kinds of lies — lies, damned lies, and statistics.”
This reflects the necessity of understanding and applying statistical concepts well
Recognizing Proper Use of Statistics
Learning to identify valid statistical evidence supporting claims
Examples of constructive use of statistics:
Research teams demonstrate drug efficacy based on trials
Statistics reveal racial bias in jury selections in historical contexts
Statistics are prevalent, but their application can vary in integrity
Learning Objectives
3.1 Basic Concepts
Identify the population of a study
Distinguish between statistics (calculated from a sample) and parameters (calculated from a population)
Classify data types as either categorical (qualitative) or quantitative
3.2 Sampling Methods
Recognize various sampling techniques
Select appropriate sampling methods
Identify common sources of bias in sampling
3.3 Experiments
Identify characteristics of well-structured experiments
Distinguish different methods of experimentation
Identify experiments that effectively control for placebo effects
Attributions
Content adapted from:
David Lippman, “Math In Society, 2nd Edition.” Licensed under CC BY-SA 4.0.
Robert Foth, Math Faculty, Pima Community College, 2021.
Links to Previous and Next Sections
Previous Section: 2.5 Expected Value
Next Section: 3.1 Basic Concepts