Those of you familiar with my recalcitrant non-conforming ways but also know that I am loathe to reject any traditionally-accepted theory without first striving to gain a mastery ofit will appreciate, then, my plans to attend the 2010 Statistics Canada International Methodology Symposium, from October 26 to 29, 2010, in my hometown (well, from the time [...]
With Statistics Canada having been criticized in the news recently, it’s good to see some of the real applications that impact Canadian businesses and lives, such as the Canadian Internet Use Survey. But I think practitioners–and the general public–still aren’t quite fulfilling “due diligence” in either citing the Statistics Canada information or in how they [...]
Yvon, selon le commissariat aux langues officielles, ni CIRA ni les programmes fédéraux n'oublige qu'il ait besoin évident: http://www.ocol-clo.gc.ca/html/faq2_f.php#q4 The following post was actually primarily a response to "Canadian Public Interest in Internet Policy and Decision Making" sent by CIRA in October, 2009. If it were a one-off survey conceived by someone at CIRA whose [...]
“Lies, damn lies, and statistics,” is a reference to the abuse of statistics to support a position. I feel that this cliché has, itself, been abused and resulted in the unnecessary malignment of statistics, which is actually an extremely powerful tool not only to characterize populations or phenomena, but to predict events, with known confidence (such as the application of probit models and logistic regression that take categorical or numeric predictor variables, such as age, income level, and preferences, that describe a customer segment and calculate the probability that customers will purchase a new product). I am even more dismayed at the cynicism that has come to surround statistics; whilst the cliché describes the intentional and unintentional abuse of statistics out of context or inappropriately with intent to influence rather than inform, most people–even those who have taken an introductory statistics course in university (perhaps especially those, since most people are thoroughly confused and intimidated by the subject after an introductory course)–do not have sufficient understanding of the theoretical bases for statistical techniques to see the power in them. Our world is not a deterministic place; even the most reliable process will occasionally yield unexpected results. Thus, it is vitally important that we can quantify the likelihood that an observation is truly a characteristic result of a phenomenon and state how confident we are that a given observation was not the result of random chance.
The following post is an exerpt from the required self-assessment after completing Walden University’s RSCH 8200 “Quantitative Reasoning and Analysis” course, which is required for all management doctoral students as part of their foundation research sequence. It is a next generation course that incorporated feedback from previous incarnations of the course and is only its [...]