How statistics can be misleading - Mark Liddell

What is the problem with making decisions based solely on organized data?

• Data does not influence decisions

• Organized data is always accurate

• Data can be manipulated to promote agendas

• Data is not persuasive enough

Tags

CCSS.RI.11-12.7

CCSS.RI.8.7

CCSS.RI.9-10.7

CCSS.RL.11-12.7

CCSS.RL.9-10.7

In the example of choosing between two hospitals, why does Hospital B become the better choice for patients in poor health?

• Hospital B had more patients in poor health

• Hospital B had more experienced doctors

• Hospital B had better facilities

• Hospital B had a higher overall survival rate

Tags

CCSS.7.SP.A.2

What is the lurking variable in the case of Simpson's paradox in the hospital example?

• The location of the hospitals

• The relative proportion of patients who arrive in good or poor health

• The cost of treatment at each hospital

• The number of doctors at each hospital

Tags

CCSS.6.SP.B.5C

CCSS.HSS.ID.A.3

What did the study in the UK initially appear to show about smokers and nonsmokers?

• Smokers had a higher survival rate

• Nonsmokers had a higher survival rate

• Smokers had a lower survival rate

• Nonsmokers had a lower survival rate

Tags

CCSS.RI.8.1

CCSS.RI.8.8

CCSS.RL.11-12.1

CCSS.RL.8.1

CCSS.RL.9-10.1

What was the lurking variable in the study of Florida's death penalty cases?

• The age of the defendant

• The gender of the defendant

• The location of the crime

• The race of the victim

Tags

CCSS.7.SP.B.3

How can we avoid falling for Simpson's paradox?

• By carefully studying the actual situations the statistics describe

• By always trusting overall numbers

• By dividing data into misleading categories

• By ignoring all statistics

What is the main concern if lurking variables are not considered when analyzing data?

• Data will be too complex to understand

• Data will be manipulated by others

• Data will be inaccurate

• Data will not be persuasive

Tags

CCSS.HSS.IC.B.3

Why is it important to consider lurking variables in statistical analysis?

• To make the data more complex

• To make the data more persuasive

• To ensure the data is always accurate

• To avoid misinterpretation of the data

What can be a consequence of not considering lurking variables in data analysis?

• Data will not be persuasive

• Data will be manipulated by others

• Data will be too complex to understand

• Data will always be accurate

Tags

CCSS.HSS.IC.B.3

What is the best way to interpret statistics to avoid falling for Simpson's paradox?

• By carefully studying the actual situations the statistics describe

• By blindly trusting the overall numbers

• By ignoring all statistics

• By dividing data into misleading categories