Tuesday, 8 October 2013

Shruti Kulkarni
046


Definition of 'Z-Score'


  • A Z-Score is a statistical measurement of a score's relationship to the mean in a group of scores. 
  • A Z-score of 0 means the score is the same as the mean.
  •  A Z-score can also be positive or negative, indicating whether it is above or below the mean and by how many standard deviations.
  • In addition to showing a score's relationship to the mean, the Z-score shows statisticians whether a score is typical or atypical for a particular data set.
  •  Z-scores also allow analysts to convert scores from different data sets into scores that can be accurately compared to each other. 
  • One real-life application of z-scores occurs in usability testing.
  • A z-score (also known as z-value, standard score, or normal score) is a measure of the divergence of an individual experimental result from the most probable result, the mean. 
  • Z is expressed in terms of the number of standard deviations from the mean value.

z = \frac {X-\mu}{\sigma} (6)
X = ExperimentalValue
μ = Mean
σ = StandardDeviation

  • Z-scores assuming the sampling distribution of the test statistic (mean in most cases) is normal and transform the sampling distribution into a standard normal distribution. As explained above in the section on sampling distributions, the standard deviation of a sampling distribution depends on the number of samples. 

  • Whenever using z-scores it is important to remember a few things:

1) Z-scores normalize the sampling distribution for meaningful comparison.
2) Z-scores require a large amount of data.
3) Z-scores require independent, random data.

z_{obs}= \frac {X-\mu}\frac{\sigma}{\sqrt{n}} (7)

n = SampleNumber