The Art of Statistics

Synopsis

'A statistical national treasure' Jeremy Vine, BBC Radio 2

'Required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force' Popular Science

Do busier hospitals have higher survival rates? How many trees are there on the planet? Why do old men have big ears? David Spiegelhalter reveals the answers to these and many other questions - questions that can only be addressed using statistical science.

Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way statistical claims can be sensationalised, particularly in the media. In the age of big data, as data science becomes established as a discipline, a basic grasp of statistical literacy is more important than ever.

In The Art of Statistics, David Spiegelhalter guides the reader through the essential principles we need in order to derive knowledge from data. Drawing on real world problems to introduce conceptual issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether serial killer Harold Shipman could have been caught earlier, and if screening for ovarian cancer is beneficial.

'Shines a light on how we can use the ever-growing deluge of data to improve our understanding of the world' Nature

Summary

Chapter 1: Data

* Summary: Introduces the concept of data, its types (qualitative vs. quantitative), and its importance in understanding the world.
* Example: A dataset of student test scores, including their names (qualitative) and scores (quantitative).

Chapter 2: Visualizing Data

* Summary: Explores graphical methods for presenting data, such as bar charts, histograms, and scatterplots.
* Example: A bar chart showing the distribution of exam scores for a class.

Chapter 3: Describing Data

* Summary: Introduces measures of central tendency (mean, median, mode) and variation (range, standard deviation).
* Example: Calculating the mean score of a group of students and the standard deviation to assess their spread.

Chapter 4: Probability

* Summary: Explains the concepts of probability and probability theory, including conditional probability, Bayes' theorem, and distributions.
* Example: Determining the probability of rolling a six on a standard six-sided die.

Chapter 5: Sampling and Inference

* Summary: Discusses sampling methods (random, stratified, etc.) and statistical inference, including confidence intervals and hypothesis testing.
* Example: Using a random sample of customers to estimate the average satisfaction rating for a product.

Chapter 6: Correlation and Regression

* Summary: Explores the relationship between two variables using correlation and regression analysis.
* Example: Investigating the relationship between the number of hours studied and exam score using linear regression.

Chapter 7: Statistical Models

* Summary: Introduces basic statistical models, such as linear regression, logistic regression, and time series analysis.
* Example: Fitting a linear regression model to predict sales based on advertising expenditures.

Chapter 8: Communicating with Statistics

* Summary: Emphasizes the importance of communicating statistical findings effectively through clear reporting, visualization, and avoiding common pitfalls.
* Example: Creating a PowerPoint presentation summarizing the results of a market research study.