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No, the opposite.

No, the opposite.

No, the opposite.

No, the opposite.

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11y ago
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11y ago

No, the opposite.

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Q: Can a deviation score with a high numerical value indicate a score close to the mean?
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Related questions

Calculate a deviation score?

27


What is the of z-score -1.0?

A z score is a value that is used to indicate the distance of a certain number from the mean of a normally distributed data set. A z score of -1.0 means that the number is one standard deviation below the mean. A z score of +1.0 means that the number is one standard deviation above the mean. Z scores normally range from -4.0 to +4.0. Hope this helps! =)


Which is better a score of 92 on a test with a mean of 71 and a standard deviation of 15 or a score of 688 on a test with a mean of 493 and a standard deviation of 150?

score of 92


Why use the T score?

T-score is used when you don't have the population standard deviation and must use the sample standard deviation as a substitute.


Does the Bayley Scales numerical score correspond to an IQ score?

no


What will the sum of the deviation score always be?

zero


What happens to the standard score as the standard deviation increase?

The standardised score decreases.


How do you calculate standard deviation with the help of z-score?

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.


When is a t test better than a z score?

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.


A z score is most affected by?

standard deviation


Is the different between an individual score and mean of the group of scores is called deviation?

No, it is called the absolute deviation.


How do you know if a z score is positive or negative?

A negative Z-Score corresponds to a negative standard deviation, i.e. an observation that is less than the mean, when the standard deviation is normalized so that the standard deviation is zero when the mean is zero.