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edit approved Nov 8, 2020 at 13:47
develarist
edit approved Nov 8, 2020 at 13:47
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Find How to find out if the two datasets are close to each other?

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).

Visually

I graphed the cdf of real data vs cdf of simulated data and try to see visually that which one is the closest.

cdf of data_a vs data_b

Above is the cdf of data_a vs cdf of data_b enter image description here

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-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

Find if the two datasets are close to each other

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).

Visually

I graphed the cdf of real data vs cdf of simulated data and try to see visually that which one is the closest.

cdf of data_a vs data_b

Above is the cdf of data_a vs cdf of data_b enter image description here

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.

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

How to find out if two datasets are close to each other?

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).

Visually

I graphed the cdf of real data vs cdf of simulated data and try to see visually that which one is the closest.

cdf of data_a vs data_b

Above is the cdf of data_a vs cdf of data_b enter image description here

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

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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).

Visually

I graphed the cdf of real data vs cdf of simulated data and try to see visually that which one is the closest.

cdf of data_a vs data_b

Above is the cdf of data_a vs cdf of data_b enter image description here

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.

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

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).

Visually

I graphed the cdf of real data vs cdf of simulated data and try to see visually that which one is the closest.

cdf of data_a vs data_b

Above is the cdf of data_a vs cdf of data_b enter image description here

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.

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

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).

Visually

I graphed the cdf of real data vs cdf of simulated data and try to see visually that which one is the closest.

cdf of data_a vs data_b

Above is the cdf of data_a vs cdf of data_b enter image description here

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.

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

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Find if the two datasets are close to each other

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).

Visually

I graphed the cdf of real data vs cdf of simulated data and try to see visually that which one is the closest.

cdf of data_a vs data_b

Above is the cdf of data_a vs cdf of data_b enter image description here

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.

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