I have the following three datasets.
data_a=[0.21,0.24,0.36,0.56,0.67,0.72,0.74,0.83,0.84,0.87,0.91,0.94,0.97] data_b=[0.13,0.21,0.27,0.34,0.36,0.45,0.49,0.65,0.66,0.90] data_c=[0.14,0.18,0.19,0.33,0.45,0.47,0.55,0.75,0.78,0.82]
data_a is real data and the other two are the simulated ones. Here I am trying to check which one (data_b or data_c) is closest or closely resembles to data_a. Currently I am doing it visually and with ks_2samp test (python).
I graphed the cdf of real data vs cdf of simulated data and try to see visually that which one is the closest.
Above is the cdf of data_a vs cdf of data_c
So by visually seeing it one might can say that data_c is more closer to data_a then data_b but it is still not accurate.
Kolmogorov-Smirnov (KS) Test
Second method is the KS-test where I tested data_a with data_b as well as data_a with data_c.
>>> stats.ks_2samp(data_a,data_b) Ks_2sampResult(statistic=0.5923076923076923, pvalue=0.02134674813035231) >>> stats.ks_2samp(data_a,data_c) Ks_2sampResult(statistic=0.4692307692307692, pvalue=0.11575018162481227)
From above we can see that statistic is lower in when we tested data_a with data_c so data_c should be closer data_a than data_b. I didn't consider the pvalue as it would not be appropriate to think of it as a hypotheses test and use the p-value obtained because the test is designed with the null hypothesis predetermined.
So my question here is that if I am doing this correctly and also is there any other better way to do it? Thank You