You could test whether several statistics that are descriptive of a distribution are the same in the subsample and the remaining sample. For example you could conduct tests for:

• mean difference
• median difference
• stochastic dominance
• different variance
• shape

While you are at it, since you are interested in similarity, I would also explore tests for equivalence of all such measures (for example, using tost), probably combining inferences from difference and equivalence tests.

Something else you may want to consider: why are you interested in this similarity? The answer to this question may help you decide which, if any, such tests you may like to explore. For example, if you sample size is smallish, you may not have enough power for the Kolmogorov–Smirnov test mentioned by soakley, although you might still have power enough to make inferences about, say, the sample mean. If you are only interested in comparing sample means, that may be OK for your purposes.

You could test whether several statistics that are descriptive of a distribution are the same in the subsample and the remaining sample. For example you could conduct tests for:

• mean difference
• median difference
• stochastic dominance
• different variance
• shape

While you are at it, since you are interested in similarity, I would also explore tests for equivalence of all such measures (for example, using tost), probably combining inferences from difference and equivalence tests.

Something else you may want to consider: why are you interested in this similarity? The answer to this question may help you decide which, if any, such tests you may like to explore. For example, if you sample size is smallish, you may not have enough power for the Kolmogorov–Smirnov test mentioned by soakley, although you might still have power enough to make inferences about, say, the sample mean. If you are only interested in comparing sample means, that may be OK for your purposes.

You could test whether several statistics that are descriptive of a distribution are the same in the subsample and the remaining sample. For example you could conduct tests for:

• mean difference
• median difference
• stochastic dominance
• different variance

While you are at it, since you are interested in similarity, I would also explore tests for equivalence of all such measures (for example, using tost), probably combining inferences from difference and equivalence tests.

You could test whether several statistics that are descriptive of a distribution are the same in the subsample and the remaining sample. For example you could conduct tests for:

• mean difference
• median difference
• stochastic dominance
• different variance
• shape

While you are at it, since you are interested in similarity, I would also explore tests for equivalence of all such measures (for example, using tost), probably combining inferences from difference and equivalence tests.

Something else you may want to consider: why are you interested in this similarity? The answer to this question may help you decide which, if any, such tests you may like to explore. For example, if you sample size is smallish, you may not have enough power for the Kolmogorov–Smirnov test mentioned by soakley, although you might still have power enough to make inferences about, say, the sample mean. If you are only interested in comparing sample means, that may be OK for your purposes.