Can someone help me find the answer for a sample n=36 with a population mean of of 76 and a mean of 79.4 with a standard deviation of 18?
T-score is used when you don't have the population standard deviation and must use the sample standard deviation as a substitute.
Bob scored 300 or 700.
The absolute value of the standard score becomes smaller.
The "z-score" is derived by subtracting the population mean from the measurement and dividing by the population standard deviation. It measures how many standard deviations the measurement is above or below the mean. If the population mean and standard deviation are unknown the "t-distribution" can be used instead using the sample mean and sample deviation.
Because the z-score table, which is heavily related to standard deviation, is only applicable to normal distributions.
T-score is used when you don't have the population standard deviation and must use the sample standard deviation as a substitute.
Bob scored 300 or 700.
When you don't have the population standard deviation, but do have the sample standard deviation. The Z score will be better to do as long as it is possible to do it.
The z score, for a value y, is (y - 18.6)/4
1.41
You need the mean and standard deviation in order to calculate the z-score. Neither are given.
The standardised score decreases.
score of 92
z-score or standard score... tells you how many standard deviations away from the mean a particular number is in relations to all numbers in a population (or sample)
A z-score cannot help calculate standard deviation. In fact the very point of z-scores is to remove any contribution from the mean or standard deviation.
The absolute value of the standard score becomes smaller.
The "z-score" is derived by subtracting the population mean from the measurement and dividing by the population standard deviation. It measures how many standard deviations the measurement is above or below the mean. If the population mean and standard deviation are unknown the "t-distribution" can be used instead using the sample mean and sample deviation.