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POD "floor" and POD "ceiling"  
Some data do not support a POD curve that goes to zero on the left, or to one on the right.  
2 + 2 = 5  
Just because you can make a statistical statement doesn't make it true, no matter how much you wish that it were.  
Parameter Estimates are NOT parameter values.  
There is a profound difference between the mathematical behavior of a function whose parameter values are defined (e.g. the FORM/SORM paradigm) and the same function whose parameter values must be estimated from data.  
How ?vs a POD Models Work  
POD (Probability of Detection) is the probability that a signal, (? "ahat") will be larger than the decision threshold.  
How hit/miss POD Models Work  
POD (Probability of Detection) as a function of size is less straightforward for binary (yes/no) data when compared with data having a continuous response (?.  
How WELL do POD Models Work?  
In reality we only get to see ONE collection of data, and from that must estimate the most likely model for the unseen and unknown and unknowable "truth."  
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 twoday short course is based on the new (2007) MILHDBK1823 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 stateoftheart techniques to analyze your enterprise data.  
MILHDBK1823A, "Nondestructive Evaluation System Reliability Assessment"  
2009 release of 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 tradeoff curves, and includes workedout examples using real Hit/Miss and ?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 "lowprobability" events.  
Repeated Inspections  
Repeated inspections do not improve Probability of Detection (POD).  
Central Limit Theorem FinePrint  
Readers have requested further explanation of when the CLT does not apply.  
PseudoProof that 2 equals 1  
Seemingly logical steps can lead to a silly conclusion. Unfortunately, not all silliness is as selfevident 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 ... ?  
GoodnessofFit  
GoodnessofFit tests, like AndersonDarling, tell you when you don't have a normal distribution.  
Rsquared ...  
... is an often misused goodnessoffit metric, where bigger isn't always better.  
Other Measures  
Rsquared isn't the only way to judge how well the model works.  
Chronology of Crack Initiation  
Tongueincheek view contains insights.  
Curse of Dimensionality  
Directsampling 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!  
IntraOcular Trauma Test  
Sometimes the best GoodnessofFit test is the easiest.  
Central Limit Theorem  
Why is the Average of nearly anything always Normal ?  
Hiking the Grand Canyon, rimtorim!  
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 InterRelationships  
There are myriad probability
distributions. But did you know that most are related to one another, and ultimately
related to the Normal?

Mail to Charles.Annis@StatisticalEngineering.com 