interpreting data lesson 13. true, but conclusions drawn from data may be correct, but they can also...
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Example The mean age of a sophomore English class will be near 17 years old? Why when most of the students are 15 or 16 years of age?TRANSCRIPT
Interpreting Data
Lesson 13
True, but…Conclusions drawn from data may be correct, but they can also be misleading. You need to carefully analyze and interpret the reliability and reasonableness of the data.
Example
The mean age of a sophomore English class will be near 17 years old? Why when most of the students are 15 or 16 years of age?
Waiting in Line
Wait Times:½ minute, 1½ minute, 2 minutes, ½ minute, ½ minute, 2 minutes, 1 minute, 1 minute, 9 minutes
Mean: _____ minutesHow many are greater than the mean?How many are less than or equal to the mean?What’s wrong with this figure?9 minutes is called an outlier.
Bias
Making the results lean toward one particular outcome.To determine which program should be cut from a school to save money, ask the PE classes. To determine how most students get to school, survey students at the bus drop off area.
What’s wrong with these methods?
A grocery store conducted a survey about the buying habits of the customers. One survey question asked, “Do you buy cosmetics here?”
Out of 100 customers surveyed, 10 answers “yes,” and 90 answered “no.” The store owners made the following prediction from this survey. Less than 10% of our customers use makeup.
Is this reasonable?
Biased or UnbiasedSurvey to determine students’ favorite sport:
Is your favorite sport football?What is your favorite sport?
Biased or UnbiasedSurvey to determine favorite class.
Asking the students during band class.Asking football players at practice.Asking every third student in line for lunch.Asking students present for freshman study hall.Asking students as they leave for CAVIT.Asking students as they are dropped off for school.
Is the chart biased or unbiased?Why or why not?
If it is biased, who would benefit from the information?
Video Lessons
Misleading DataUsing Data to make Decisions – DrivingToo Much Noise? How Misleading Data Warps Climate Debate
Assignment