|All of the jokes on this page pertain to “Distributions.” This includes topics such as bell curves, the Cauchy Distribution, other distributions and moments amongst others.|
|I1||Bell Curve||THE TRUE BELL CURVE – The distribution of SUCCESS in life in relationship to AGE follows a true bell curve:|
At age 5, success is not peeing in your pants
At age 10, success is having friends in many, many places
At age 16, success is having a driver’s license and no moving violations
At age 20, success is having sex but harboring a variety of anxieties about it
At age 35, success is having money to pay cash for a turbocharged Porsche Carrera GT
At age 50, success is having money to pay cash for turbocharged Porsche Carrera GT
At age 65, success is having sex but harboring a variety of anxieties about it
At age 70, success is having a driver’s license and no moving violations
At age 75, success is having friends in many many places
At age 80, success is not peeing in your pants
** Thanks again to my colleague Dr. Jazzbo Johnson for suggesting this hilarious joke as it was related to him by a friend. **
|I2||Distributions||THE BELL CURVE MEETS THE WELL CURVE|
BELL: Fancy meeting you underneath me. I never did understand why someone perverted er, an INVERTED me and created you. You aren’t worth much!
WELL: You must have had your BELL rung! One of your allegedly famous applications is approximating a sampling distribution for certain hypothesis tests and the power curves for many of these tests are well, a WELL CURVE.
BELL: Oh WELL, I forgot that! More critically, WELL, your central tenancy is all messed up. Neither your mean or median represents you. Only your modes at the extremes characterize you. My curve is neat and tidy with all those indices identical. That is a real BELL RINGER!
WELL: WELL BELL, you are still living in the 18th and 19th centuries. You don’t realize how distributions are changing. For example, the distribution of wealth is becoming WELL since the middle class is disappearing and only the extremely wealthy and the impoverished poor are increasing at the ends. Also, the approval ratings of elected officials is becoming WELL since feelings are polarized at the extremes with not much in the middle. I could go on and on.
BELL: WELL, you are threatening the limits of my practical range! Maybe, we can talk again under more NORMAL circumstances.
WELL: BELL you had your MOMENTS but we shall talk again. Meanwhile let us tell all statisticians to tie each set of our ends together and use the combined distributions as a CHRISTMAS TREE ORNAMENT! Good Day!
** Well, folks, how many of you have even heard of the WELL CURVE? I was doing some Web surfing and found this interesting article by Jim Pinto printed in the San Diego Mensan, Aug. 2003. Seemingly, Mr. Pinto has coined the expression WELL CURVE for an inverted Normal Curve and touted its usefulness. Maybe this curve is becoming so prominent that it should now be included in statistics textbooks. Anyways, this conversation between the two curves is strictly my own little piece of “humorous” statistical nonsense.” **
|I3||Cauchy Distribution||What’s the question the Cauchy distribution hates the most? Got a moment?|
** This is only funny if you are steeped in mathematical statistics. Thanks go out to S. Gomatam for contributing this odd one. **
|I4||Normal Distribution||A middle aged man suddenly contracted the dreaded disease kurtosis. Not only was this disease severely debilitating, but he had the most virulent strain called leptokurtosis. A close friend told him his only hope was to see a statistical physician who specialized in this type of disease. The man was very fortunate to locate a specialist but he had to travel 800 miles for an appointment.|
After a thorough physical exam, the statistical physician exclaimed, “Sir, you are indeed a lucky person in that the FDA has just approved a new drug called mesokurtimide for your illness. This drug will bulk you in the middle, smooth out your stubby tail, and restore your longer range of functioning. In other words, you will feel ‘NORMAL’ again!”
** This shows how weird statistical humor can get. This is my own joke so go easy on the feedback! **
|I5||Normal Distribution||How is a normal probability distribution like a lion?|
They both have a MEAN MEW.
** Thanks are in order to Cynthia Gadol, an AP Statistics Teacher at Thomas Jefferson Classical Academy, for sending me this neat little pun. She claims she heard it years ago from Professor Rolf Bargmann at the University of Georgia. Cynthia, I have a reply for you: Q. How does a lion differ from a normal probability distribution? A. A lion cannot go three standard deviations in pitch above or below its mean mew!! Oh well, this craziness makes the medicine go down a lot easier. **
|I6||Normal Distribution||Upon being mugged several weeks ago by the Cauchy Distribution, the Normal Distribution had these comments:|
“I am still not back to normal yet but I do have my moments and the point of inflection in my voice has improved considerably. I just wish I had taken some ordinance along that night to fend off the attacker.”
** I did not know that distributions could engage in such outrageous behavior. The Statistics Crime Scene Investigative Unit (SCSIU) has mad major recommendations in distribution attacks of this nature. They have strongly urged any distribution to “Always be willing to give up a few moments to an attacker and the attacker like a panhandler will usually stroll away from. **
|I7||Normal Distribution||A boy asked his statistician father, “Why is my body not well-proportioned just like my brother’s?”|
His father’s response, “Because, when you mother had your pregnancy, its distribution was skewed!!”
** Does Medical Science know about this? Does this mean if your pregnancy is normally distributed you will have a perfectly proportioned baby? Thanks to Okunola Olajide Ezekiel for sending this. **
|I8||Moment||If the 2nd moment about the mean is the variance, the 3rd moment is skewness, and the 4th moment is kurtosis, what is the kth moment?|
That’s easy….the kth moment is a KODAK MOMENT!!!
** I better pause a MOMENT and duck before I tell you this one is mine. **
|I9||Moment||DAY OF THE QUIZ:|
Professor: “OK students, you have fifteen minutes to plot the bivariate distribution between A and B, fifteen minutes to compute the correlation between A and B, and 5 SECONDS to compute the kurtosis of B.”
One student stands up very worried: “Excuse me Professor, how can we possibly compute a kurtosis in 5 SECONDS?”
The Professor looks at the class very reassuring: “No need to be worried, kids, IT TAKES ONLY A MOMENT!!”
** I want to take this moment to thank Marcello Gallucci of the Free University in the Netherlands for this little tidbit of humor. **
|I10||Moment||An elderly statistician complained to a younger statistician one day that he was having a “senior moment” when he forgot what integrating the normal probability density function produced. The younger statistician said not to worry because all he had to do was to set “junior moment” on his moment generating function and it would spit out “area under the curve.” The elderly statistician stared vacantly for a few seconds then confessed that this moment generating function had no such setting and suggested that the younger statistician may have also just had a “senior moment.”|
** This little exchange I wrote is dedicated to Professor Robert V. Hogg of the Statistics Department at the University of Iowa who taught me all about moment generating functions. Professor Hogg was an outstanding instructor and his upbeat attitude and interjection of fun into his lectures first gave me the notion that just maybe statistics did not have to be dry and humorless. Many, many years later my daughter was able to have the experience of taking one of Professor Hogg’s classes at Iowa as well.**
|I11||Normal Curve||FOR THOSE WHO KNOW EVERYTHING THERE IS TO KNOW ABOUT STATISTICS|
Question: Is the Normal Curve every a Skewed Distribution?
Answer: Always!! The Lower Half of the Normal Curve is Negatively Skewed and the Upper Half is Positively Skewed!!
** This is an unfair trick question. A Gestalt psychologist would urge you look at the normal curve as a single entity and not as two curves treated separately. But if you want to have some fun with your statistics professor ask him this question and see how he responds. Then after you give him the answer tell him he must begin thinking outside of the box. He may even give you extra credit!! I may be placed in a box and shipped away after this one. **
|I12||Normal Curve||In a Standard Normal Curve, the total area under the curve is ONE. Why then has it never been proven that no matter how far you go out away from the middle of the score scale at 0 in either direction there will always be some portion of the area under the curve beyond either point?|
NO ONE HAS EVER WALKED OUT THAT FAR YET!!!
** Of course this was a bold face lie and it has been proven. In all seriousness though, it does seem like a contradiction that you can keep accumulating area under the curve as you keep moving out away from the middle and yet never exceed ONE. In simple language, just think of the concept of limits in mathematics. As you move in either direction toward minus infinity and plus infinity, the area under the curve does increase but only approaches ONE as a limit. Certainly not as exciting as proof of Fermat’s Last Theorem a few years back but it does seem counter intuitive and excites your block just a wee bit. **
|I13||Normal Curve||Why does the Normal Curve not need any degrees of freedom?|
It is very content and smug about its status… It is ALREADY a t-Curve with infinite degrees of freedom so a few more would not help!
** Many students think of these two curves as separate entities because the Normal Curve is usually taught first. However, remember that the Normal Curve is the limiting case of a t-Curve with infinite degrees of freedom. Thus, the t-Curve, in reality, is the more general concept. in successive graphs of t-Curves, as the degrees of freedom increases starting with 3, the area in the tails shrink and the are in the middle of the curve increases approaching the Normal curve as a limit. Even at a degrees of freedom of 30 for the t-Curve in a moderately scaled drawing, graphs of the two curves are practically indistinguishable to the naked eye and the difference would only be detected by a high definition camera. **