Suppose with convergence in probability. For real and , the elementary inequality gives
First let , then let . The characteristic functions converge pointwise to that of , which is continuous at zero. The Lévy continuity theorem gives convergence in probability implies convergence in distribution.
For the converse, let have the fair Bernoulli distribution, put , and set for every . All have the same probability distribution as , so . But always, hence
There is no convergence in probability to . In this example does have convergence in probability to : the counterexample concerns the converse with the specified limit , as usual. If one interprets the question as denying convergence in probability to any limit, take for even and for odd . Its probability distribution is still constant, while the two subsequences have distinct limits from almost sure convergence, ruling out any common limit in probability by uniqueness of a limit in probability.

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