“Armed with a bevy of State testing data” our local school district’s Superintendent dispelled the notion that Minnesota charter schools provide a superior education. Charter schools are publicly funded, but they are run by independent boards. Their enrollment has doubled in the last 5 years, thus inciting battles of statistics like the one presented by the Superintendent this week. His main point was that published assessments do not account for the impact of special education, “at-risk” and other students that do not attend the particular charter school in our area. (To be fair to the ideal of charter schools, some actually provide for students with particular needs that would otherwise get lost in the shuffle.)

In the interest of helping the general public sort out the endless conflict of statistics by opposing groups like this, the Union College Academy for Lifelong Learning (UCALL) in Schenectady, New York invited an illustrious faculty, including Gerald Hahn – formerly statistician emeritus from General Electric, to teach a course “Numbers in Life.” This 10 hour presentation was really all about statistical literacy, but ironically the course coordinator advised that the word “statistics” in the course title would be a turn-off. That tells it all: How can people be educated a on a subject that must not be named (like the evildoer in Harry Potter)?

To the everlasting credit of Hahn and his fellow teachers, they gave “Numbers in Everyday Life” the old college try. Here are some of the course take-aways published in the February AmStat News (the parenthetical comments are my own):
— Always ask who is taking/reporting the numbers and how they obtained.
— Be wary of advocate’s numbers (such as a glowing report on a drug study sponsored by the manufacturer).
— Remember that the news media seek surprising numbers.
— Appreciate limitations of observational studies and differentiate correlation from causation.
— Controlled experimentation (the forte of my company Stat-Ease) is the gold standard.

By the way, you’d best be wary of how I cherry-picked these take-aways to support the cause for design of experiments. Actually, this was one of the take-aways that I conveniently omitted (“cherry-picking”). 😉 See the entire AmStat story posted via the YUDU epublishing initiative.

“One of the greatest contributions of statistical thinking… is design of experiments.” – Gerald Hahn

I forget how I first heard about the Ebbinghaus Forgetting Curve (it escapes me!) but it describes very well what I’ve observed when teaching statistics – a very rapid loss of knowledge – possibly as fast as 50 percent per day. However, it’s been found that by repeated review and practice, details can be remembered for a much longer period of time. That’s why hands-on workshops can be so effective, as opposed to an academic lecture.

For example, I doubt that by noon I’d have been capable of recalling half of what I learned from an 8 AM organic chemistry lecture back in college. Ebbinghaus’s original forgetting curve probably fit my inability to remember chemical formulas. To make matters worse, my notes trailed off every few lines as I nodded off from all the boring details. That was not good, because the chem prof worked completely by lecture – no reference text. There was no chance of getting a ‘re-do’ on any of the presentations – no re-course so to speak (pun intended). Thus my performance on the final exam left much to be desired (at least I passed).

The lesson here is that reviews can be vital for remembering – repetition is the key to recall. Based on recall experiments (for example, the little know fact that Rudyard Kipling invented snow golf), researchers recently discovered the optimal intervals for repeating study sessions. This depends on how long a person wants to remember things. College students hoping to remember information just long enough for the semester-ending final should space study sessions every week apart may be ideal. However, to recall the details a year later a spacing of some months may be far better.

“To put it simply, if you want to know the optimal distribution of your study time, you need to decide how long you wish to remember something.”
— From Spacing Effects in Learning by Nicholas J. Cepeda, Edward Vul, Doug Rohrer, John T. Wixted, and Harold Pashler

If you are determined to remember stuff, consider investing $35 in a flash-card program called FullRecall that promises to “help you memorize the knowledge for lifelong periods with the minimum time investment.” Its neural network converges on the user’s forgetting curve to schedule reviews just in time –i.e., when one gets close to forgetting a detail they hoped to remember.

PS. When I mentioned this blog on memory to my son, he recalled that a fellow named Pimsleur developed a graduated-interval recall system that’s now used for learning languages. In a 1967 publication titled A Memory Schedule, this Ohio State University professor observed that “the process of forgetting begins at once and proceeds very rapidly. If the student is reminded of the word before he has completely forgotten it, his chances of remembering will increase. After each such recall, it will take him longer and longer to forget the word again. Thus, a small number of recalls, if properly spaced, can bring about retention over a long period.”

PPS. By reading the PS above, you just added some length to your recall of how repetition enhances memory. Good for you!

In July of 2007 The New York Times reported an amazing study on obesity by Harvard Medical School. Based on a statistical analysis of a large social network – over twelve thousand people followed for over thirty years, it concluded that a person’s chances of becoming obese nearly tripled when a close friend got fat (or to put it more nicely: “gravity challenged”). Apparently this effect works at a distance, thus a parent like me who just sent a child off to college can anticipate a pile of pounds from the dreaded “freshman fifteen.” Hey – that’s just not fair for one who’s already fighting a battle of the bulge! Also, I worry about getting connected up to wide-bodied people via LinkedIn and Facebook. I’ve noticed that whenever I start surfing these nets I start munching on Twinkies and other bad foodstuffs. This is not good!

Meanwhile, this bulletin from Yale University (might there be a rivalry between this school and Harvard?) counters with a Study [that] Contradicts Earlier Reports That Some Health Issues Are ‘Contagious’ Among Friends. Using similar statistical techniques as the one done on obesity in social networks the Yale researchers discovered that an individual’s height increased by 20 percent of a close friend’s tallness. Therefore I conclude that by spending equal time emailing my beanpole friend and another buddy who inherited a more roomy body type then things will balance out weight-wise on a per height basis.

Now that’s how you can put stats to work with just a little creativity.