1. 1-4: Cautions Using Statistics
Homework 4: p , 9-12
Learning Objectives:
Learn to raise questions from surveys
Understand how data from statistics can be misleading or false
4: Stem and Leaf Plot
Entry Task: Matching B
2: Quartiles A C D
1: Outlier
1: Outlier 3: Box and Whisker Plot
2: Quartiles 4: Stem and Leaf Plot
3: Box and Whisker Plot

2. Concept: How Statistics can Mislead
Given the data, which color car do customers prefer?
Note: The data from the table correlates to the chart
TABLE: The clear winner is White
CHART: The winner is Red
10%

3. Example 1: Misleading Bar Graphs
The graph shows customer satisfaction with different brands. Explain why the graph is misleading
The scale on the vertical axis starts at 76. This exaggerates the difference’s between the sizes of the bars.
Discuss how the graph is exaggerated on the y-axis
Discuss how Brand B may be considered a favorite with only ~5% different from lowest

4. Student Led Example 1: Misleading Bar Graphs
A high school principal is considering whether to institute a school uniform policy. She sends out a survey to the students at her high school to get their options.
Write how would you feel about having a school uniform policy?
Write a conclusion from the data
Determine whether the display gives an accurate picture of the survey results
Write about it!
Write about it!
Prepare to Discuss

5. Example 2: Misleading Line Graphs
Below is actual data for Warrenton, OR.
How can comparing the two charts create confusing results?
Clouds! Partially sunny, mostly cloudy, etc. Weather doesn’t stay the same ALL DAY. How many hours are needed to determine the weather conditions?
Source:
city-data.com/city/Warrenton-Oregon.html

6. SLE 2: Misleading Line Graphs
Below is actual data for Warrenton, OR.
How can comparing the two charts create confusing results?
Snow is considered precipitation. The two are not mutually exclusive
Source:
city-data.com/city/Warrenton-Oregon.html

7. Example 3: Data Fishing and Correlations
Statement: Cancer is caused by eating Apples
Event A
Event B
Not True but Plausible
Anyone who has ever contracted cancer has eaten and apple at least once in their life.
This is called flawed correlation
A→B
Not even a little true → Obsurd!
Anyone who has ever eaten an apple has contracted cancer
B→A

8. Student Led Example 3: Data Fishing and Correlations
Please access Mr. Hansen’s Website: Tab: Applied Algebra Section: Chapter 1 Link: Button “Lesson 1-4: SLE 3” Take 10 minutes to review the statistics provided on the webpage. Focus Questions: What possible correlations do you find to be flawed? What possible errors are found in the data? What can be done to increase data accuracy?
Useful Definitions:
Flawed Correlation: when unrelated events are said to have a cause and effect to each other
Data Fishing/Data Dredging: the use of data mining to uncover pattern in data that can be presented as statistically significant, without first devising a specific hypothesis as to the underlying cause.
Data Mining: the practice of examining large data bases in order to generate new information

9. End of Lesson
According to this map, which city would received the higher amounts of rainfall? Sequim, WA or Billings, MT?
Sequim
Billings
Sequim is pronounced: SKWIM

10. End of Lesson

11. End of Lesson
Take a moment to reflect on this lesson. What is the overall, underlying theme of everything covered today?

12. End of Lesson
Sequim
Billings
Aberdeen