While the initial standard should be based on a distribution of some kind, and possibly not even the normal, each personal grade should not be based on it.
The normal distribution is very important in statistical analysis. A considerable amount of data follows a normal distribution: the weight and length of items mass-produced usually follow a normal distribution ; and if average demand for a product is high, then demand usually follows a normal distribution. It is possible to show that when the sample is large, the sample mean follows a normal distribution. This result is important in the construction of confidence intervals and in significance testing. In quality control procedures for a mean chart, the construction of the warning and action lines is based on the normal distribution.
Because of the Central Limit Theorem, the mean value of sets of observations is distributed Normally. Irrespective of the underlying distribution of a variable, the distribution of a sum (or mean) of such variables tends towards the Normal distribution. So, for sufficiently large samples, all distributions can be approximated by thye Normal distribution. One of the consequences is that the distribution has been studied extensively and a lot is known about tests based on it.
To test how well observations agree with some expected distribution. The latter is often non-parametric so that tests based on the Gaussian (Normal) distribution are not appropriate.
The ideal sample size depends on a number of factors:how far from Normal the underlying distribution is.how close you need to get to a Normal distribution - in terms of the decision(s) that might be based on it and the cost of making an error.the rarity of the characteristic that you wish to study. (You might need a large sample just to ensure that you get representatives that have whatever characteristic you are studying.)
It is not negative. it is positively skewed, and it approaches a normal distribution as the degrees of freedom increase. Its shape is NEVER based on the sample size.
The normal distribution is very important in statistical analysis. A considerable amount of data follows a normal distribution: the weight and length of items mass-produced usually follow a normal distribution ; and if average demand for a product is high, then demand usually follows a normal distribution. It is possible to show that when the sample is large, the sample mean follows a normal distribution. This result is important in the construction of confidence intervals and in significance testing. In quality control procedures for a mean chart, the construction of the warning and action lines is based on the normal distribution.
empirical distribution is based on your observation of out comes, it is based on real data. on the other hand theoretical is base on your theory regarding the distribution and the parameters, (i.e. normal/exponential...., u=5 vs u .5....and so on)
The answer will depend on the consequences of making the wrong decision based on the statistics.
Because of the Central Limit Theorem, the mean value of sets of observations is distributed Normally. Irrespective of the underlying distribution of a variable, the distribution of a sum (or mean) of such variables tends towards the Normal distribution. So, for sufficiently large samples, all distributions can be approximated by thye Normal distribution. One of the consequences is that the distribution has been studied extensively and a lot is known about tests based on it.
A normal distribution with a mean of 200 and a deviation of 20 can be plotted as a bell-shaped curve, as shown in the figure below. Superimposed on the figure, the distribution of the arithmetic mean of samples of size n=4, 25 and 100 can be plotted as shown in the figure below. The arithmetic mean distribution for n=4 is a much narrower distribution than a normal distribution, since it is based on a small sample size. As the sample size increases, the distribution becomes wider and more similar to the normal distribution.
To test how well observations agree with some expected distribution. The latter is often non-parametric so that tests based on the Gaussian (Normal) distribution are not appropriate.
The ideal sample size depends on a number of factors:how far from Normal the underlying distribution is.how close you need to get to a Normal distribution - in terms of the decision(s) that might be based on it and the cost of making an error.the rarity of the characteristic that you wish to study. (You might need a large sample just to ensure that you get representatives that have whatever characteristic you are studying.)
It is not negative. it is positively skewed, and it approaches a normal distribution as the degrees of freedom increase. Its shape is NEVER based on the sample size.
I would measure the level of the customer satisfaction as far as the physical distribution is concerned based on the feedback that they will provide.
The wind turbines are on fixed foundations therefore it would be extremely difficult to modify their distribution.
It plots people on a "normal distribution" where the majority are in the middle, usually corresponding to a C/C+ and there are fewer people as you go out towards the ends (B's and A's, or D's and F's). Essentially, if people don't naturally fall on a normal distribution it artificially fits them onto one and shifts their grades up or down. It's based upon an assumption that the majority of people are and should be graded as mediocre and fewer should fall to either side. In practice though there is often such diversity in averages due to a host of different variables/conditinos, even among very large classes/schools/districts, that this approach tends to artificially inflate or deflate grades.
The link below goes to a discussion on railroad grades. According the folks responding, a 3.5% grade would be considered "plenty steep," although grades of up to 4.5% are noted. Based on my limited experience in the east, grades for mainlines would be well under 3.5%.