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Friday, February 07, 2020

Using Statistics to Support Your Research

Statistics can provide excellent evidence for your paper.  However, unless they are used appropriately, they can undermine your argument and can even be destructive. In addition, it’s easy to reinforce cognitive biases with cherry-picked statistics without realizing what you’re doing.  The coupling of cognitive bias with flawed statistics was explored by Daniel Kahneman and Amos Tversky, and was part of their Nobel prize-winning findings. 

Here are a few guidelines for using statistics in your paper.

The key is to be aware of how statistical reasoning occurs and where it might be faulty.  Faulty statistical reasoning can be harmful.  It can lead to causal relationships or conclusions that are unwarranted, inaccurate, or deceptive.  Even if the presentation of the statistics is compelling, and even if the source seems to be reliable, they can be inaccurate. As you analyze, keep in mind when / how you might be making errors when analyzing data.

The Manipulated and "Sanitized" Statistic.  Numbers can be manipulated to make the facts seem to conform to one’s agenda.  For example, the College Board manipulated the SAT scores in 996 and it made it appear that math and verbal scores improved, when in reality, the performance was about scene.

Needlessly precise and hard to read:  need to put it in a form that it is easier to decipher and compare.

The Meaningless Statistic.  Exact numbers can be used to quantify something so inexact, vaguely defined, or difficult to count that it could only be approximated.  The exact number looks impressive, but it can hide the fact that certain subjects (domestic abuse, eating habits, use of narcotics, shopping, sexual preference) cannot be quantified exactly because respondents don't always tell the truth, because of denial, embarrassment, or merely guessing. Or they respond in ways they think the researcher expects.

The Vagueness of the Average.  The mean, median, and mode are three measures of central tendency (the intermediate, or middle, value in a set of numbers) can be used in inconsistent and inappropriate way in order to make .

How to say it’s the average:  The core of the problem comes from the fact that there are ways of reporting "average" - mean, median, mode

Unethical uses of "averages”.  people can tend to use the average that serves their purposes

The Distorted Percentage Figure.  Percentages are often reported without explanation of the original numbers used in the calculation.  Another fallacy in reporting percentages occurs when the margin of error is ignored.  This is the margin within which the true figure lies, based on estimated sampling errors in a survey.

False Ranking.  This happens when items are compared on the basis of poorly-defined criteria.  Unless we know how the ranked items were chosen and how they were compared (the criteria), a ranking can produce a scientific-seeming number based on a completely unscientific methods.

Drawbacks of Data Mining.  Many highly publicized correlations are the product of data-mining.  In this process, a software program searches databases and randomly compares one set of variables (say, buying habits) with another set.  From these countless comparisons, certain relationships, or associations, are revealed (perhaps between green tea frappucino drinking and pancreatic cancer risk).  At one retail company, a correlation between diaper sales and beer sales, presumably because young fathers go out at night to buy diapers.  The retailer then displayed the diapers next to the beer and reportedly sold more of both.

The Biased Meta-Analysis.  In a meta-analysis, researchers look at a whole range of studies that have been done on one topic (say, the role of high-fat diets to cancer risk).  The purpose of this "study of studies" is to decide on the overall meaning suggested by these collected findings. 
These are just a few of the many areas of bias in the use of statistics. With new algorithms being developed and the quest for meaningful pattern recognition in machine learning and deep learning, it’s important to recognize that bias can creep in at any point, especially if you have a predetermined idea about the result, or have a vested interest.



Sunday, February 02, 2020

Sunshine Cleaning (2008): Sisters and Entrepreneurship

The independent, low-budget film, Sunshine Cleaning, (Dir. Christine Jeffs, 2008), was well received at film festivals and by critics. It received six non-winning nominations and two winning nominations for film awards. The film won “Outstanding Achievement in Casting – Low Budget Feature – Drama/Comedy) and also Women Film Critics Circle Awards “Best Woman Storyteller.”  The film’s budget was capped at $5 million. The box office proceeds came in at $17.3 million, which does not include Internet / app distribution.

Writer: 
Megan Holley

Cast (partial listing):

Rose (Amy Adams)
Norah (Emily Blunt)
Joe (Alan Arkin)
Oscar (Jason Spevack)
Mac (Steve Zahn)

Synopsis:

After deciding her gifted by quirky young son should attend private school rather than continue to be bullied, Rose Lorkowski, a mom who has been employed with a maid service provider, discovers that crime scene and biohazard cleanup pays many times more than her current job. So, with the help of her free-spirited but unreliable younger sister and baby-sitting support from her hapless entrepreneur father, she launches Sunshine Cleaning. The first few jobs are a bit overwhelming, especially since the two sisters know absolutely nothing about hazardous materials, bloodborne pathogens, or personal protective equipment. They persevere, however, and start to build the business.  As they clean up the aftermath of accidental deaths, accidents, criminal acts, and suicides, the sisters start to confront some of the darker issues of their own lives, including the suicide of their own mother, the erratic parenting of their father, and the tendency to become involved in relationships that have no hope of a positive outcome.





Analysis:

Set in Albuquerque, New Mexico, the light has that clear, yellow-gold clarity of northern New Mexico mountains, that contrasts with a clear blue sky and a chaparral / desert pavement ground. It’s earthy and realistic, lending the film a sense of authenticity.

What I like about the movie is the entrepreneurial spirit in a time of desperate challenges; the financial collapse of 2008 is not explicitly mentioned, but its presence is palpable. The uneasy relationship between two sisters and their well-intentioned but hapless father is also very touching. The sisters, through sheer force of will (and love for family), overcome the sickening nature of the crime scenes and bio-hazard zones.



In doing so, they are able to see the murky shapes in the recesses of their conscious minds, and to let the undifferentiated masses of emotions long suppressed come to the surface and untangle themselves.

Through the contact with death, many times due to the suicide of someone, the suicide of their mother emerges.  They come to realize that many of the patterns and behaviors they’ve had over the years have been in response to that traumatic loss.



And, as time goes on, they courageously face the memories and the feelings, they start on the tough work of cleaning up the ultimate bio-hazard zone, grief and loss.

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