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Where where <math>X_iO_i</math> is the an observed value, frequency and <math>mu_iE_i</math> is the an expected value, and <math>sigma_i</math> is again (theoretical) frequency asserted by the expected value, but with different Greek letter representing it to be cutenull hypothesis.
There was a time senior year when a t-squared analysis on In the case of the 508 games played in the four years of cribbage competition, our expected number of victories wins for each showed that Zach was, statistically, a superior cribbage player to Jonathanis 254. Once that point was reached, Zach felt his point was proven We can plug this value and lowered his level the observed number of play, allowing Jonathan wins into the equation to win a few games and regain a bit of dignity... at least statistically speaking.determine the <math>\chi^2</math> value for the analysis: :<math>{(264 - 254)^2 \over 254}+{(244 - 254)^2 \over 254}</math>
But then, what exactly is a "t-squared analysis"? Perhaps it is a fancy term for a t-test, a test to determine if the means of two normally distributed data sets are equivalent? If so, such a test was misapplied in this use, as there are no normal distributions to test. Now, a ''chi-squared'' analysis would have been something else entirely . . . the proper test to use in fact . . . but how much faith could we put in a statistician who could not even get the name of the test correct?
And to think, someone could pass the psychology major with flying colors and but There was a time senior year later have no idea what he is talking about. Running when a chit-squared analysis would be on the number of victories each showed that Zach was, statistically, a cop-out in analyzing this problemsuperior cribbage player to Jonathan. Multiple T-squared analyses, on the other handOnce that point was reached, might help shed some light on just why Zach is perceived by the majority felt his point was proven and lowered his level of campus as having just that "special something" that elevates his game well above that play, allowing Jonathan to win a few games and regain a bit of the J-dawgdignity... at least statistically speaking.
→In Search of the Truth: Who's Better?: in progress
Interpreting data by a chi-square analysis is done using the formula:
:<math>\chi^2 = \sum_{i=1}^k \left(\frac{X_i-\mu_in}{\sigma_i}\right(O_i - E_i)^2\over E_i}</math>
==Cribbage moments==
