**Convergence in measure** is either of two distinct mathematical concepts both of which generalize
the concept of convergence in probability.

Let be measurable functions on a measure space The sequence is said to **converge globally in measure** to if for every

and to

On a finite measure space, both notions are equivalent. Otherwise, convergence in measure can refer to either global convergence in measure or local convergence in measure, depending on the author.

Throughout, *f* and *f*_{n} (*n* **N**) are measurable functions *X* → **R**.

- Global convergence in measure implies local convergence in measure. The converse, however, is false;
*i.e.*, local convergence in measure is strictly weaker than global convergence in measure, in general. - If, however, or, more generally, if
*f*and all the*f*_{n}vanish outside some set of finite measure, then the distinction between local and global convergence in measure disappears. - If
*μ*is*σ*-finite and (*f*_{n}) converges (locally or globally) to*f*in measure, there is a subsequence converging to*f*almost everywhere. The assumption of*σ*-finiteness is not necessary in the case of global convergence in measure. - If
*μ*is*σ*-finite, (*f*_{n}) converges to*f*locally in measure if and only if every subsequence has in turn a subsequence that converges to*f*almost everywhere. - In particular, if (
*f*_{n}) converges to*f*almost everywhere, then (*f*_{n}) converges to*f*locally in measure. The converse is false. - Fatou's lemma and the monotone convergence theorem hold if almost everywhere convergence is replaced by (local or global) convergence in measure.
- If
*μ*is*σ*-finite, Lebesgue's dominated convergence theorem also holds if almost everywhere convergence is replaced by (local or global) convergence in measure. - If
*X*= [*a*,*b*] ⊆**R**and*μ*is Lebesgue measure, there are sequences (*g*_{n}) of step functions and (*h*_{n}) of continuous functions converging globally in measure to*f*. - If
*f*and*f*_{n}(*n*∈**N**) are in*L*^{p}(*μ*) for some*p*> 0 and (*f*_{n}) converges to*f*in the*p*-norm, then (*f*_{n}) converges to*f*globally in measure. The converse is false. - If
*f*_{n}converges to*f*in measure and*g*_{n}converges to*g*in measure then*f*_{n}+*g*_{n}converges to*f*+*g*in measure. Additionally, if the measure space is finite,*f*_{n}*g*_{n}also converges to*fg*.

Let *μ* be Lebesgue measure, and *f* the constant function with value zero.

- The sequence converges to
*f*locally in measure, but does not converge to*f*globally in measure. - The sequence where and (The first five terms of which are ) converges to
*0*globally in measure; but for no*x*does*f*converge to zero. Hence_{n}(x)*(f*fails to converge to_{n})*f*almost everywhere.

- The sequence converges to
*f*almost everywhere and globally in measure, but not in the*p*-norm for any .

There is a topology, called the **topology of (local) convergence in measure**, on the collection of measurable functions from *X* such that local convergence in measure corresponds to convergence on that topology.
This topology is defined by the family of pseudometrics

where

In general, one may restrict oneself to some subfamily of sets

still defines a metric that generates the global convergence in measure.

Because this topology is generated by a family of pseudometrics, it is uniformizable. Working with uniform structures instead of topologies allows us to formulate uniform properties such as Cauchyness.