I have one distribution of size 30.

This are results (ROC-AUC for example) from training a neural network with the same hyperparameters but since they are randomly initialized the result is always a little bit different.

Then I train the same network with other hyperparameters and only want to do that for fewer runs. Lets say for 5 runs.

My null hypothesis is that the smaller runs distribution is not smaller than the distribution with 30 runs (one side test).

What kind of statistical significance test would be the best to compare these small distributions?

PS: At the moment I am using Mann Whitney U Test. Is there anything better?

I have one distribution of size 30.

This are results (ROC-AUC for example) from training a neural network for 30 times in a row with the same hyperparameters but since they are randomly initialized the result is always a little bit different.

Then I train the same network with other hyperparameters and only want to do that for fewer runs. Lets say for 5 runs.

My null hypothesis is that the smaller runs distribution is not smaller than the distribution with 30 runs (one side test).

What kind of statistical significance test would be the best to compare these small distributions?

PS: At the moment I am using Mann Whitney U Test. Is there anything better?

I have one distribution of 30 samples.

This are samples from training a neural network with the same hyperparameters but since they are randomly initialized the result is always a little bit different.

Then I train the same network with other hyperparameters and only want to do that for fewer runs. Lets say for 5 runs.

My null hypothesis is that the smaller runs distribution is not smaller than the distribution with 30 runs (one side test).

What kind of statistical significance test would be the best to compare these small distributions?

I have one distribution of size 30.

This are results (ROC-AUC for example) from training a neural network with the same hyperparameters but since they are randomly initialized the result is always a little bit different.

Then I train the same network with other hyperparameters and only want to do that for fewer runs. Lets say for 5 runs.

My null hypothesis is that the smaller runs distribution is not smaller than the distribution with 30 runs (one side test).

What kind of statistical significance test would be the best to compare these small distributions?

PS: At the moment I am using Mann Whitney U Test. Is there anything better?