New Stuff


	mh1823 POD CD
	... a self-contained CD with R installed with the necessary ancillary R routines, the installed mh1823 POD software, and the example datasets – everything. You put the CD in the drive, make a desktop icon and you’re up and running in 30 seconds. If you already made the icon, put the CD in the drive and click the icon and you're running in 5 seconds.
	How the LogLikelihood Ratio Criterion Works
	Constructing Confidence Bounds on Probability of Detection Curves based on how likely some alternatives to the maximum likelihood would be.
	POD Short Course/Workshop
	This two-day short course is based on the new (2007) MIL-HDBK-1823 and uses the mh1823 POD software. The course provides the latest methods for measuring your NDE system's effectiveness and the workshop will use these state-of-the-art techniques to analyze your enterprise data.
	MIL-HDBK-1823, "Nondestructive Evaluation System Reliability Assessment"
	2007 Update – describes procedures for acquiring NDE data and statistical methods for analyzing it to produce POD(a) curves, 95% confidence bounds, noise analysis, and noise vs detection trade-off curves, and includes worked-out examples using real Hit/Miss and ā data.
	Themes ...
	... I'm not a philosopher - but I, like you, do occasionally ruminate on the human condition.
	Hubris
	"I don't need to understand your problem to solve it."
	The Great Misunderstanding
	Both statisticians and engineers recognize the mathematical competence of the other, and this is the cause of The Great Misunderstanding.
	Quantitative Nondestructive Evaluation
	It isn't the smallest crack you can find that's important – it's the largest one you can miss.
	Round Robin Testing ....
	... testing can sometimes make you see something that isn't there.
	False Positives and the ROC Curve ...
	The relationship between POD and False Positives depends on more than the inspection itself. It also depends on the frequency of defectives in the population being inspected.

	Will ...
	If you think that you dare not, you don't ...

	Probability and Statistics ...
	... are not one and the same. The differences are not nuanced. They are Apples and Oranges.

	Reading List
	I am often asked to recommend a "good statistics text." Here are a few that I refer to often.

Two Secrets of Success

Monte Carlo Oversights
	Most Engineering Monte Carlo simulations ignore the distinction between parameter values, and estimates of parameter values, resulting in a gross underestimation of the probability of "low-probability" events.

Repeated Inspections
	Repeated inspections do not improve Probability of Detection (POD).

Central Limit Theorem Fine-Print
	Readers have requested further explanation of when the CLT does not apply.

Pseudo-Proof that 2 equals 1
	Seemingly logical steps can lead to a silly conclusion. Unfortunately, not all silliness is as self-evident as this example.

The "Most Probable Point" is a fiction
	First Order, and Second Order Reliability Methods (FORM/SORM) are based on a demonstrably false premise of a "Most Probable Point."
Contrasting the Statistical with the Mathematical Properties of NESSUS/FORM
	Engineers see reliability as an optimization problem on a known response surface. Statisticians view it differently.
"Choosing" the Right Distribution
	There is considerable folklore about choosing statistical distributions, as you might select the appropriate club from your golf bag.


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.
Did you know ... ?

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.
Chronology of Crack Initiation
	Tongue-in-cheek view contains insights.
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 play fast and loose with joint, marginal, and conditional probabilities - to our detriment.
Correlation:
	It's a lot more - and less - than you may think
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
Random Fatigue Limit on a P/C
	Pascual and Meeker's RFL solves an old problem: how to have a runout model go through (rather than under) all the runout data.
Free Translations
	Not too Statistical, but still Fun! Check it out!
InterOcular Trauma Test
	Sometimes the best Goodness-of-Fit test is the easiest.
Central Limit Theorem
	Why is the Average of nearly anything always *Normal* ?
Hiking the Grand Canyon, rim-to-rim!
	Words and pictures are insufficient.
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?