INTERNALS

This is for the information of all the faculty members that PTU under its letters No. 60552/321001 dated 22/03/2016 and 60555/321001 dated 22/02/2016 returned internals grades of session 2015-16 to large number of institutes alleging that these did not follow Normal Distribution Curve. As you know our name was also in the list and we had to rework on the entire set of internals once again.
Therefore, this time while preparing your internals make sure that they follow the new rules which have been briefed to all of you time and again. A few faculty attended the workshop at PTU also on this subject and can further guide others. What is normal distribution curve is also given below for brushing up ;
The Normal Distribution Curve and Its Applications
The normal distribution, or bell curve, is most familiar and useful to teachers in describing the frequency of standardized test scores, how many students earned particular scores. This is not just any distribution, but a theoretical one with several unique characteristics:
• It is always symmetrical, with equal areas on both sides of the curve.
• The highest point on the curve corresponds to the mean score, which equalsthe median and the mode in this distribution.
• The area between given standard deviation units (represented byperpindicular lines in the diagram below) includes a determined percent area. Because of the curve’s symmetry, the percent area is the same as the percentfrequency of test scores.

The mean (the perpendicular line down the center of the curve) of the normal distribution divides the curve in half, so that 50% of the area under the curve is to the right of the mean and 50% is to the left. Therefore, 50% of tests cores are greater than the mean, and 50% of test scores are less than the mean. The figure above shows that 34.13% of the area is between the mean and +1 or -1SD units, called a z score. Therefore a total of 68.26% (34.13% x 2) of the test scores fall between +1 and -1 SD.(Try working out other percentages of area under the curve between two standard deviation lines or the total percentage to left or right of a standard deviation line.)
Example application: All the second-graders in a school took an IQ test with a mean of 100 and a SD of 15. An administrator wants to determine what percent of the examinees should score between 1 SD above (100 + 15 = 115 IQ) and1 SD below (100 – 15 = 85 IQ) the mean. Since the percent area under the curve equals the percent frequency of scores, 68.26% (34.13% x 2) of the students should score between 85 and 115 on the IQ test. In addition, 15.87%(50% – 34.13% = 15.87%) will score above a score 115 and below 85.
On the same IQ test, one second-grader received a score of 145. The teacher knew this was an exceptional score but wanted to compare his score to those of other students. The score of 145 is +3 SD units above the mean(100 + 15 + 15 + 15 = 145). The area under the normal distribution curve to the left of this score is 99.87% (50% + 34.13% + 13.59% + 2.15% = 99.87%). Therefore, this student scored better than 99.87% of the other test-takers. This statistic is also referred to as a percentile.
Of course not all test score distributions are normally distributed. They can be skewed, i.e. have a disproportionate number of people who do very well or very poorly. This would be the case if a test was too easy or too hard for the testing population. However, standardized tests are designed so that the outcome follows a normal distribution curve.