I NEED IT BY TOMORROW AT 130 PM TO SUBMIT IT PLEASE HOW TO L

I NEED IT BY TOMORROW AT 1:30 PM TO SUBMIT IT. PLEASE!

HOW TO LIE WITH STATISTICS BY Darrell Huff

Book\'s link below, copy it and paste it, and look for chapter 10. Pages 122-142.

http://www.horace.org/blog/wp-content/uploads/2012/05/How-to-Lie-With-Statistics-1954-Huff.pdf

INSTRUCTIONS

1) Read the text.

2) Write a short summary of the chapter 10, highlighting key points and recounting memorable examples. Your summary should follow the following format.

Organize your summary into two parts: (1) Key Ideas, which outline the main points the author is making, and (2) Memorable Examples that illustrate these points. You probably won\'t fill a page.

3) Find one example from contemporary sources (within the past 2 years) that illustrates one of the concepts from the book. Briefly explain why you believe this is a \"bad example\" of statistics. (Provide a complete bibliographic citation for your source.

4) Find another example from contemporary sources (again, within the past 2 years) that shows statistics being done right. (After all, not all statistics are lies.) Again, briefly explain why you believe this is a \"good example,\" and provide complete bibliographic citation for your source.

Solution

not all statistical information that you may come upon can be tested with the sureness of chemical analysis or of what goes on in an assayer\'s laboratory

But you can prod the stuff with 5 simple questions

Who says so?

about the first thing to look for is bias- the laboratory with something to prove for the sake of a theory¿, a reputation, or a fee

How does he know?

watch out for evidence of a biased sample, one that has been selected improperly

aske the question we dealt with in an early chapter: Is this sample large enough to permit any reliable conclusion?

Is it big enough to mean anything?

are there enough cases to add up to any significance?

Whats missing?

you wont always be told how many cases. The absence of such a figure, particulartly when the source is in an interested one, is enough to throw suspicion on the whole thing

similarly a correlation given without a measure of reliability is not to be taken very seriously

Did somebody change the subject?

when assaying a statistic watch out for a switch somewhere between the raw figures and the conclusion

One thing is all too often reported as another

The figures would be more informative if there were some indications of whether they are means or medians

The post hoc variety of pretentious nonsense is another way of changing the subject without seeming to

The change of something with something else is presented as because of.

Does it make sence?

this often cut a statistic down to size when the whole rigmarole is based on an unproved assumption.