Q: A sample consists of n equals 16 scores How many of the scores are used to calculate the range?

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T score is usually used when the sample size is below 30 and/or when the population standard deviation is unknown.

You should think of a dependent t as being a single-sample t on the difference scores. This gives it 1 less than the number of differences as the df. Say you have before/after scores for 10 people. You have 20 scores, but the test is done on the differences, of which you have 10 and that means 9 df. You typically obtain df from n - 1, as you do in this case, you just need to be careful to think of this as the number of pairs and not scores.

You calculate the z-scores and then use published tables.

The Independent Samples T Test compares the mean scores of two groups on a given variable.

149

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z=(x-mean)/(standard deviation of population distribution/square root of sample size) T-score is for when you don't have pop. standard deviation and must use sample s.d. as a substitute. t=(x-mean)/(standard deviation of sampling distribution/square root of sample size)

If, by SX, is meant the sum of the scores, then the answer is 48/4 = 12

Sorry this is meaningless. What exactly is the question?

T score is usually used when the sample size is below 30 and/or when the population standard deviation is unknown.

You should think of a dependent t as being a single-sample t on the difference scores. This gives it 1 less than the number of differences as the df. Say you have before/after scores for 10 people. You have 20 scores, but the test is done on the differences, of which you have 10 and that means 9 df. You typically obtain df from n - 1, as you do in this case, you just need to be careful to think of this as the number of pairs and not scores.

true

149

0.50

sum of scores: 24 mean of scores : 24/4 = 6 squared deviations from the mean: 9, 4,4,9 sum of these: 26 sample variance: 26/4 = 6.5

n= 25 scores from a population with mean =20

You calculate the z-scores and then use published tables.

Simple frequency distribution is a method of organizing large data sets into more easily interpreted sets. An example is organizing sample test scores by the individual scores.