Naked Statistics

Wheelan emphasizes that statistics is the backbone of decision-making in modern society—shaping everything from medical diagnosis to online advertising, from public policy to sports strategy. He stresses that most people unknowingly rely on statistical models every day: Google autocomplete uses probabilities to predict what you’ll type next; Spotify recommends music based on statistical similarity to your past behavior; Amazon decides which products to display based on click-through probabilities; and hospitals determine staffing needs by analyzing seasonal patient surges. Companies like UPS and FedEx use statistical route-optimization models to reduce fuel use. Airlines forecast overbooking probabilities to avoid empty seats. Political analysts interpret polling data to project elections, adjusting for likely voter models and sampling biases. Wheelan underscores that even life-critical systems—such as early cancer detection, emergency room triage algorithms, and self-driving cars—are built entirely on statistical probability. He argues that understanding these ideas is crucial not only to avoid being misled but to meaningfully engage in modern society, where nearly every important argument involves data.

Turning Raw Numbers Into Useful Stories

Wheelan explains descriptive statistics by showing how they compress massive datasets into simple, interpretable summaries. He gives examples such as:
• E-commerce: Amazon tracks every click, search, and purchase from millions of shoppers but summarizes performance through conversion rates, average order value, and customer lifetime value.
• Weather forecasting: Meteorologists use 30 years of weather data to calculate average temperatures, rainfall distributions, extreme-value probabilities, and climate trends.
• Public health: Agencies summarize flu outbreaks using positivity rates, reproduction numbers, and median ages of infection.
• Sports: Basketball teams analyze tens of thousands of possessions to produce averages like points per possession, effective field-goal percentage, and player efficiency rating (PER).
He also explains why variability matters as much as central tendency. Consider two investment portfolios: both average a 7% annual return, but one swings from –20% to +30% while the other stays between +5% and +9%. The second portfolio is far safer. Wheelan uses the example of airline arrival times: the average delay might be 10 minutes, but if some flights are delayed two hours, passengers suffer more than that average suggests. Standard deviation uncovers the hidden volatility behind simple averages. He also uses box plots to show income dispersion across neighborhoods and scatterplots to illustrate how studying hours relate to exam scores. These examples teach readers that describing a dataset requires more than just one number—it's about understanding its shape, spread, and extremes.

When “The Average” Misleads

Wheelan elaborates on how reliance on averages leads to flawed systems. He cites:
• Urban planning: City planners often use “average commuting time” even though traffic peaks are driven by outliers—massive congestion events—not typical days.
• Healthcare diagnostics: “Average” blood glucose levels ignore patients whose highs and lows average out to normal but whose swings are dangerous.
• Economic policy: Policymakers looking at average wage growth overlook the fact that most wage gains may go to the top 10%, hiding stagnation for the majority.
• Automobile safety: ...