Why is the score distribution analysis done?
› Recruiters shouldn’t have to flip between the job
description and a candidate’s score report to identify how
well the candidate fits the role.
› At a glance, a recruiter should be able to quickly identify
if a candidate’s score falls within the “acceptable fit or
average” zone of the scale.
Ergo, Mocha’s score distribution analysis does
Identify the candidate amongst average score
› Standard Deviation
› Bell Curve
Standard Deviation (SD)
SD is used to indicate how many candidates are spread out
in relation to the average(mean) score.
› A low SD value close to 0, indicates candidates close to
› A high SD value is spread out, away from the average
View the percentage of candidates in the range close to the
average value (here: 0)
Using the standard deviation formula, we can
calculate high and low SD values
Number of candidates
Candidates Test scores
› Test – General Aptitude
› Appeared candidates - 5
› Test score - 100
1. Find the mean:
(92+88+80+68+52)/5 = 76
Mean (x) = 76
2. Find the deviation from the mean:
92-76 = 16
88-76 = 12
80-76 = 04
68-76 = -8
52-76 = -24
3. Square the deviation from the mean:
(16)^2 = 256
(12)^2 = 144
(04)^2 = 16
(-8)^2 = 64
(-24)^2 = 576
4. Find the sum of the squares of the deviation from the mean:
256+144+16+64+576 = 1056
5. Divide the number of total candidates to find the
1056/5 = 211.2
6. Find the square root of the variance:
Thus the Standard deviation of the test score is 14.53
and mean is 76
Therefore, to Benchmark the candidate with respect to the average score,
we display the analysis in the form of a Bell curve.
The standard deviation slots will be in the range of 14.53 above and below the
mean/average score of 76.
e.g. A candidate named Greg scoring 65 will be seen among 68% of the bulk
scoring close to the average marks.