A "factoid" is a nonexistent word invented by CNN and expropriated here as just the right nonword for the situation.

• Frequentists and Bayesians - There is a continuing debate among statisticians over the proper definition of "probability."
• "Probabilistics" - There is more to Monte Carlo simulation than replacing constants with probability densities.
• Bivariate Normal - Here is a simple algorithm for sampling from a bivariate normal distribution.
• Goodness-of-Fit  Goodness-of-Fit tests, like Anderson-Darling, tell you when you don't have a normal distribution.
• R-squared ... is an often misused goodness-of-fit metric, where bigger isn't always better.
• Other Measures  R-squared isn't the only way to judge how well the model works.
• Curse of Dimensionality  Direct-sampling Monte Carlo requires the number of samples per variable to increase exponentially with the number of variables to maintain a given level of accuracy.
• convergence in distribution - We engineers are familiar with convergence to a point, but what of convergence to a distribution?
• extreme value distributions - The largest, or smallest, observation in a sample has one of three possible distributions.  This is another example of "convergence in distribution."
• Joint, Marginal, and Conditional Probability - We engineers often ignore the distinctions between joint, marginal, and conditional probabilities - to our detriment.
• Correlation - When the correlation between two variables is zero, they're not related.  Right?  Wrong!
• Outliers ...Often infuriating, these can be very informative too.
• Wrong Grid?  Choosing the wrong grid can undermine your analysis, mislead your audience, and make you look foolish.
• Bayesian Thinking ... including an example from NDE
• IntraOcular Trauma Test  Sometimes the best Goodness-of-Fit test is the easiest.
• Central Limit Theorem - Why is the Average of nearly anything always Normal?
• Bayesian Updating - We use Bayesian Statistics every day without knowing it.
• Sums of Random Variables - Sometimes you need to know the distribution of some combination of things.  Here's an example.
• Distributional Inter-relationships - There are myriad probability distributions.  But did you know that most are related to one another, and ultimately related to the Normal?
• Bootstrapping - Bootstrap and Jackknife algorithms don't really give you something for nothing. They give you something you previously ignored.
• Bartlett correction (external link) - A Bartlett correction is a scalar transformation applied to the likelihood ratio statistic that yields an improved test statistic with chi-squared null distribution of order O(1/n), as compared with order O(1) for the LR.