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How to Lie with Statistics, Podcast #2

Posted by Jennysha Cruz in Statistics - Miles - C on

Memebers: 
Jenny
Jaaz 
Sophia


​Article links:
 Sophia's- http://www.statisticshowto.com/misleading-graphs/ “The times leave the rest behind"
Jazz's- http://gizmodo.com/how-to-lie-with-data-visualization-1563576606
Jenny's-http://www.statisticshowto.com/misleading-graphs/ “Unemployment rate under president Obama” 
LINK TO PODCAST! (sorry it actually isn't a video)

How to Lie with Statistics: Book Meeting 3

Posted by Nia Hammond in Statistics - Miles - C on

podcast 3
This is our third and final meeting podcast. 
​(reach chapter 8 to the end)

Group Members
Monisha Das
Alexis Mccormick
Alaina Silverman
Nia Hammond

Discussed:
Chapter 8 - causation and correlation differences, making assumptions/conclusions, using big names to sound "better"
Chapter 9 - graphic on page 103, "statisticulating" family incomes, misleading percentages, graphic on p. 110-111, geometric average
Chapter 10 - questions we must ask, "does this all make sense?"
The book as a whole

How To Lie With Statistics Pt. 2

Posted by Klarissa Hudson in Statistics - Miles - C on

​Group Members Present: Klarissa & Symone
Pages Read: 47 - 91
Pages For Next Week: 92 - 142

Podcast Points:

  1. Choose one of the quotations inside the front cover and discuss how it relates to the Introduction. - Klarissa

    1. “Round numbers are always false.” - Samuel Johnson

  2. List as many sources of sample bias as you can that are mentioned in Chapter 1 and provide an example of each.

    1. Average Yaleman, Class of ‘24 makes $25,111 a year.

  3. Put the second paragraph on Page 18 (“A river cannot….”) into your own words.

    1. “A river cannot rise above its source.” To me means that you can’t get something more out of what you already have, which is not true. This paragraph elaborates on this by concluding that in different data sets, we’re able to get way more information than we’ve been told. Thus, the saying a river cannot rise above its source can not be true all of the time.

  4. What is the advantage of a stratified random sample and what difficulties does it pose, according to this chapter?

    1. Advantages:

      1. You can be sure that your samples are appropriately proportioned

    2. Difficulties:

      1. Each unit must only fit in one stratum

  5. Explain why advertisers often rely on a very small sample to substantiate their claims.

    1. If advertisers were to use large sample sizes then it would be harder to substantiate their claims and ideas.

  6. What does the author mean on Page 45 when he says, “Hardly anybody is exactly normal in any way…?”

    1. What I took from the quote “hardly anybody is exactly normal in anyway” is that there are plenty of outside factors that can screw up data and paint a different picture. So nothing or no person is always normal all of the time, just like data. A clear example of this can be looking at data that suggests people who go to sleep before 10 pm receive higher tests scores. Sure, sleep is important but other factors could influence this conclusion. Things like what they ate the night before the test, what they had for breakfast, what they did in the morning, etc.

Sources:
http://gizmodo.com/how-to-lie-with-data-visualization-1563576606
http://passyworldofmathematics.com/misleading-graphs/
How To Lie With Statistics pt.2

How to Lie with Statistics Part 3

Posted by Arshelle Johnson in Statistics - Miles - C on

Group Members- Jordyn, True, Haneef and Arshelle 

In this podcast we discussed the last two chapters and also article that related back to the book. Furthermore we discussed the rules of statistics and what to look for when you are analyzing a data set. We all seemed to agree with the points that each group member brought up in the discussion.  
Dumbtrue

How To Lie With Statistics: Recording #2

Posted by Bailey Collins in Statistics - Miles - C on

Bailey, Nomi, and Jian were all present in this discussion.

During the discussion we brought up some of the questions that were asked about after our first podcast. Then we discussed chapters four and five. They were all about how averages can skew or balance a normal distribution, and how a graph can be manipulated to favor a standpoint. I don't have my group mates' links to the articles they used, but we talked about it in the podcast. We all talked about our personal favorite way to look at averaging different situations, and brought up lots of examples where one type of average would be better for the situations, or that would not be manipulated by outlier data. I don't believe there were many questions after the discussion.



STAT-010

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2014-15

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