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Showing posts with the label statistics

What is Validity?

Validity is a mighty big word. It seems to have its own meaning for each subject, though most center on the notions of correctness, consistency, conformity, and accuracy.   In fact, just Google, “whatchacallit validity” to get another definition.   Below are a few notions of important definitions. a.        As a state, as in being authentic or genuine. b.        As a force, for example legally. c.        As a measurement, as implying it does measure what is purported. d.        In research, as in the accuracy of measurements. e.        As proof, as in obeying the laws of accepted logic and premises. f.         As approved, as by an authority. g.        In statistics, as following and satisfying statistical tests. h.        In methodology, as in properly observing accepted procedures. i.          In prediction, as in how a test predicts another criterion. j.          In construct, as in the degree to which inferences can be made or how two tests closely compare.

Lies, Damned Lies, and all That

Over a century ago, British prime minister Benjamin Disraeli penned ,”There are lies, damned lies, and statistics.”   So true this is I’ve advocated for years my all students should take a Stat course.   We’ve advanced and now it is safe to say there are Lies, damned lists, statistics, and campaign promises. You can tell because whenever a campaign promise is kept, it becomes actual news. Even the official remarks on it as though one finally happened.   A regular flurry of press releases issue forth as if from a snow machine.   Remarkably, every election season most people vote for these promises knowing all the while they will not likely become reality.   Elections, therefore, are the single greatest example of hope by mankind – or are they simply legalized gambling events?    (You bet your future on outcomes you don’t expect. 😊 )

Poll Dancers

Predictions of elections reside in a world of experts, the poll dancers.   Let’s check ‘em out.   Most predicted a Clinton win by about 3% of the vote.   Nate Silver, the guru of statistics and outcome predictions, predicted chances for Clinton win at 70%. Other outlets predicted a Clinton win at 85%.   Even the odds-makers (bookies) gave her at least 2-1 odds for a win*. Some more so. Indeed, so confident was her team that Hillary Clinton left early from home for her hotel room near their NYC HQ anticipating the prospect of making an early winning announcement.    All were wrong.   Only a very few predicted a Trump win, and they were all discounted.   OK. Analysts will be analyzing away for years, among them the so very many who were wrong. They will conjecture, debate, write articles, write papers, write books, and appear on television, ad nauseam . The point here is not to give our own description of why.   Our point is about the “who.”    The “who” were acknowled

Driverless cars

You’ve heard the news about the latest technology, driverless cars and trucks*.   Can it happen?   It has.   Already four states have made it legal.    Accidents have happened, and promoters have added radar to the detection array. Google’s explanations are amusing, calling it a misunderstanding and a learning experience.   But there is yet another serious hurdle.   It is statistical.   Statistics #1.   If suddenly we changed to driverless cars, there would be accidents, lots of them.   The software would require multiple tunings. This takes time and testing; even the process of updating takes time. Accidents would continue.  Scrutiny would increase, the barrage of them keeping this always in mind. On the basis only of accidents and their visual statistics, the program might possibly be abandoned.   But if not... folks would not be driving their cars anymore.   Cars would become mere taxis.   The thrill would be gone.   Car sales would sag, with people replacing them only as