The good news when we want to hear is that you never stop learning.
For example, a certified teacher of history and geography. He has five years of primary education plus seven years of secondary education plus three or four years of graduate school plus one year probation. He had pass a contest quite difficult (if bin, anyway!) He has several years of professional experience (say ten). It makes us grosso modo-twenty-five years to learn continuously. And although he has not finished!
Suppose that teacher will be placed in the idea to enroll in a master planning abroad, in a college English, such as the University of New Orleans. Well let me tell you, he can still learn a lot! For some it takes a statistics course, he has great chances to quickly become expert in humility.
Let me explain.
, confidence intervals, z-value, binomial probability, value of alpha over two, sample mean, t-distribution, sigma, number of degrees of freedom, standard deviation of the sample means, chi-square (X2) distribution, factorial k over factorial n, null hypothesis, rejection region, alternative hypothesis, confidence level, analysis of variances, Y intercept of the regression line, total probability of error in the t-distribution, beta (=type two error), continuous variable (as opposed to discrete variable), class intervals, conditional probability, central limit theorem, deterministic distribution, expected value of the sample distribution of the mean, margin of error, estimated regression line, cumulative probability of the normal distribution, finite correction factor, skewness, critical value of z, approximated standard deviation of a sampling distribution of means, sum of squares for errors, sum of squares for Treatment, mean sum of squares, etc..
All this is a series of words that are supposed to make sense. The problem occurs when the teacher explain to you how it is calculated:
Standard deviation of a normal distribution:
Estimation of the sample size:
Confidence intervals for proportions:
Standard deviation of a sample mean:
Cumulative Probability Of The normal distribution :
Z-value:
Same thing but more complicated (the other version was too simple):
Sampling standard deviation:
Sum of squares for treatment:
Chi-square value:
This is when you finally understand the absolute misery of the boy to whom you explain something for the third time and which still does not understand: to avoid crying, he concentrated on the front of the teacher, provides a blank look and nods his head gently. Until it stops. The semester ends
late November. I'm done with nocturnal panic and nightmares where I continued on standard deviation curve distribution with a sigma in each hand and z-scores in their eyes ...
And I can finally return to my illusions to know something.
For example, a certified teacher of history and geography. He has five years of primary education plus seven years of secondary education plus three or four years of graduate school plus one year probation. He had pass a contest quite difficult (if bin, anyway!) He has several years of professional experience (say ten). It makes us grosso modo-twenty-five years to learn continuously. And although he has not finished!
Suppose that teacher will be placed in the idea to enroll in a master planning abroad, in a college English, such as the University of New Orleans. Well let me tell you, he can still learn a lot! For some it takes a statistics course, he has great chances to quickly become expert in humility.
Let me explain.
is one thing to take a course on urban transportation, urban planning history or yet the property tax. Each new concept, even taught in English is more or less equivalent to a first in France over the past twenty or more years of preliminary studies. It is quite another for a ball in math, from drowning in inferential statistics. It is immersed in an abstract world and hostile, which nothing has any connection with anything ever seen before.
Apart from the Greek alphabet ...
Small anthology of words to say: Sample standard deviation Apart from the Greek alphabet ...
, confidence intervals, z-value, binomial probability, value of alpha over two, sample mean, t-distribution, sigma, number of degrees of freedom, standard deviation of the sample means, chi-square (X2) distribution, factorial k over factorial n, null hypothesis, rejection region, alternative hypothesis, confidence level, analysis of variances, Y intercept of the regression line, total probability of error in the t-distribution, beta (=type two error), continuous variable (as opposed to discrete variable), class intervals, conditional probability, central limit theorem, deterministic distribution, expected value of the sample distribution of the mean, margin of error, estimated regression line, cumulative probability of the normal distribution, finite correction factor, skewness, critical value of z, approximated standard deviation of a sampling distribution of means, sum of squares for errors, sum of squares for Treatment, mean sum of squares, etc..
All this is a series of words that are supposed to make sense. The problem occurs when the teacher explain to you how it is calculated:
Standard deviation of a normal distribution:
Estimation of the sample size:
Confidence intervals for proportions: 
Standard deviation of a sample mean: 
Cumulative Probability Of The normal distribution : 
Z-value:
Same thing but more complicated (the other version was too simple): 
Sampling standard deviation: 
Sum of squares for treatment: 
Chi-square value: 
This is when you finally understand the absolute misery of the boy to whom you explain something for the third time and which still does not understand: to avoid crying, he concentrated on the front of the teacher, provides a blank look and nods his head gently. Until it stops. The semester ends
late November. I'm done with nocturnal panic and nightmares where I continued on standard deviation curve distribution with a sigma in each hand and z-scores in their eyes ...
And I can finally return to my illusions to know something.
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