A Distribution-Free Approach to Stochastic Efficiency Measurement with Inclusion of Expert Knowledge
Table 13
Hypothesis tests for mean differences of efficiency scores. Sample 1 is denoted as the “Control group” and sample 2 is denoted as the “Virtual group”.
Simulation 1
Simulation 2
Simulation 3
Simulation 4
Control group
Virtual group
Control group
Virtual group
Control group
Virtual group
Control group
Virtual group
DEA
Mean
Variance
Observations
Pearson correlation
Hypothesized mean difference
Df
14
14
14
14
Rank-sum test
−3.09
−3.2146
1.3688
1.7213
stat
P(T ≤ t)two tail
0.00012
0.00042
0.3266
0.1212
critical two tail
CCP efficiency evaluation
Mean
Variance
Observations
Pearson correlation
Hypothesized mean difference
Df
14
14
14
14
Rank-sum test
−1.8873
−3.0072
2.136
2.0117
stat
P(T ≤ t)two tail
0.042
0.005
0.0072
0.1041
critical two tail
DCF
Mean
Variance
Observations
Pearson correlation
Hypothesized mean difference
Df
Rank-sum test
−1.8873
−2.9657
1.8665
1.2236
stat
P(T ≤ t)two tail
0.05396
0.0059
0.08633
0.1682
critical two tail
The Rank-sum test shown previously is used to determine if the two samples being tested are of the same population. If they are of the same population, then we can conclude that the two frontiers for both the samples respectively, are one, and the same or that they consistently overlap one another, thus they can be assumed to be of the same surface.