Statistics is all about computing and deriving the data set for any specific event or the occurrence of any observation, which can have a direct or indirect impact on any activity at a corporate level or on the economy at a macro level. Obviously, corporates will have data with millions and trillions of frequencies. In order to cop up with such large data sets, statisticians use hypothesis testing to reach the conclusions. In this, they convert the raw data obtained from various sets into standard datasets, which can be easily comparable and analysis can be done over them. The process of converting raw data to a standard score is termed as standardization.
We can interpret that student score is 0 distance (in units of standard deviations) from the mean, so the student has scored average.
We can interpret that student has scored above average – a distance of 1 standard deviation above the mean.
Z score ranges from -3 to 3, while the T score ranges from 20 to 80.
2 Data Size When Population is known or above 30, one can use Z score When the population is not known or the sample size is less than 30, the T score is used.
3 Mean An average is always zero. An average is always 50.
4 Range It Ranges from -3 to 3. It ranges from 20 and 80.
9 With the increase in size With the increase in size, the Z score tends to be used With the increase in size, its usefulness reduces.
Both Z score vs T score is part of hypothesis testing under the normal distribution. If you have a set of measurement scores on different measures using Z-scores you can tell how the scores are placed in their distributions. Then you can compare them. Standardization of scores is an extensively used procedure in the field of research and planning as they help in comparison of various test scores. Standardizing scores before combining them is helps a researcher to get better and comparable results.