1. Turning Information into Wisdom
Chapter 17
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2. Wisdom Gained
Information developed through the use of statistics has …
enhanced our understanding of how life works,
helped us learn about each other,
allowed control over some societal issues, and
helped individuals make informed decisions.
Nearly every area of knowledge has been advanced by statistical studies.
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3. 17.1 Beyond the Data
Extent to which data descriptions can be generalized rests on two issues:
What group of individuals was measured?
Whether or not randomization was used to assign conditions.
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4. Random, Representative, or Restrictive Sample?
“Inferences to populations can be drawn from random sampling studies, but not otherwise.”
But true random samples almost impossible to obtain.
Fundamental Rule for Using Data for Inference is that available data can be used to make inferences about a much larger group if the data can be considered to be representative with regard to the question(s) of interest.
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5. Randomized Experiments, Observational Studies, and Causal Conclusions
Most common error by media: conclude a causal relationship established when not warranted by the way the study was conducted.
Rule for Concluding Cause and Effect is that cause-and-effect relationships can be inferred from randomized experiments, but not from observational studies.
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6. Using Nonstatistical Considerations to Assess Cause and Effect
Here are some hints that may suggest cause and effect from observational studies:
There is a reasonable explanation of cause and effect.
The connection happens under varying conditions in a number of studies.
Potential confounding variables are ruled out by measuring and analyzing them.
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7. 17.2 Transforming Uncertainty Into Wisdom
The field of statistics exists because we live in a world filled with uncertainty and variability
As individuals, we need to make personal decisions.
As a society, we want to have some control over things.
As intelligent and curious beings, we want to understand things.
As social and curious beings, we want to know about other people.
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8. 17.3 Making Personal Decisions
Think about decision in framework of hypothesis testing and consider consequences of errors.
H0: I will be better off if I take no action.
Ha: I will be better off if I do take action.
Type 1 error: taking action when would have been better off not doing so.
Type 2 error: taking no action when would have been better off taking action.
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9. Example 17.2 Surgery or Uncertainty
Doctor discovers lump and cannot tell if malignant or benign. Can have affected organ removed with surgery or wait/see if lump continues to grow/spread.
H0: The lump is benign. Ha: The lump is malignant.
Statistical information may be available to assess likelihood of each hypothesis and doctor could provide reasonable probabilities. Then must weigh possible consequences of each choice.
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10. 17.4 Control of Societal Risks
Statistical studies can be used to guide policy decisions.
Lawmakers, government regulatory agencies, and other decision makers must weigh decisions and their consequences.
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11. Example Go, Granny, Stop?
Efforts to improve driving safety often based on results of statistical studies.
Studies have shown wearing a seat belt likely to reduce chances of death or serious injury, driving after consuming alcohol increases risk of an accident.
Laws passed to help protect drivers and passengers based on findings. But can be a trade-off between protection and personal freedom.
Article: “Visual Field Loss Ups Elderly Car Crashes”
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12. Example 17.5 Smokers Butt Out
Debate over health risks of breathing smoke from other peoples’ cigarettes largely statistical in nature,
New info released at end of 2000 added fuel to fire.
After > 10 years of active antismoking measures, lung cancer rate in California was down substantially compared to the rest of the United States.
Article: “Anti-Tobacco Measures Lessen Cancer”
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13. 17.5 Understanding Our World
Statistical studies are done to help us understand ourselves and our world, without involving any decisions.
Scan any major news source and find reports of many interesting studies done to help us understand the world.
Many studies are exploratory in nature and results are controversial. That’s part of why they make interesting news.
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14. Example 17.7 Give Her the Car Keys
Memory is a fascinating ability, and most of us wish we had more of that ability.
Studies about memory may eventually help us understand ways to improve it.
Article: “Women Remember Item Location Better”
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15. 17.6 Getting to Know You Curious about how others think and behave.
What do they do with their time?
Are we in the majority with our opinions on controversial issues?
Are people basically honest?
Many questions answered by surveying random or representative samples.
Most national governments have agencies that collect samples to answer some of these questions on a routine basis.
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16. Example 17.8 Lifestyle Statistics
U.S.Census Bureau collects voluminous data on many aspects of American life. Ongoing “Current Population Survey” polls a random sample of U.S. households on wide variety of topics. Trends in lifestyle decisions can be tracked.
Estimated median age at first marriage higher than ever before. In 1994, median age at first marriage was 26.7 years for men and 24.5 years for women, approx. 3½ years higher than median age in 1970 (23.2 years for men, 20.8 years for women).
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17. 17.7 Words to the Wise
1. A representative sample can be used to make inferences about a larger population, but descriptive statistics are the only useful results for an unrepresentative sample.
2. Cause and effect can be inferred from randomized experiments, but not from observational studies, where confounding variables likely to cloud the interpretation.
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18. 3. A conservative estimate of sampling error in a survey is the margin of error 1/n. Provides a bound on the difference between true proportion and sample proportion that holds for at least 95% of properly conducted surveys.
4. The margin of error does not include nonsampling error, such as errors due to biased wording, nonresponse, etc.
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19. 5. When individuals measured constitute whole population, no need for statistical inference because truth is known.
6. A significance test based on a very large sample is likely to produce a statistically significant result even if true value is close to the null value. Wise to examine the magnitude of parameter with a confidence interval to determine if result has practical importance.
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20. A significance test based on a small sample may not produce a statistically significant result even if true value differs substantially from null. Important the null hypothesis not be “accepted” on this basis.
When deciding how readily to reject the null hypothesis (what significance level to use), important to consider consequences of type 1 and type 2 errors. If a type 1 error has serious consequences, the level of significance should be small. If a type 2 error is more serious, a higher level of significance should be used.
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21. 9. Examining many hypotheses could find one or more statistically significant results just by chance, so find out how many tests were conducted when you read about a significant result. Common in large studies to find that one test attracts media attention, so important to know if that test was the only one out of many conducted that achieved statistical significance.
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22. 10. Sometimes read researchers surprised to find “no effect” and study “failed to replicate” an earlier finding of statistical significance. Possible explanations: Sample size too small and the test had low power. Or, result in first study based on a type 1 error -- likely if effect was moderate and was part of larger study that covered multiple hypotheses.
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