A random sampling function Sample: U &#x2192; {0, 1} for a key universe U is a distinguisher with probability. If for any given assignment of values v(x) to the keys x &#x03B5; U, including at least one non-zero v(x) &#x2260; 0, the sampled sum &#x2211;{v(x)|x &#x03B5; U &#x005E;. Sample(x) = 1} is non-zero with probability at least &#x0391;. Here the key values may come from any commutative monoid (addition is commutative and associative and zero is neutral). Such distinguishers were introduced by Vazirani [PhD thesis 1986], and Naor and Naor used them for their small bias probability spaces [STOC'90]. Constant probability distinguishers are used for testing in contexts where the key values are not computed directly, yet where the sum is easily computed. A simple example is when we get a stream of key value pairs (x1, v1), (x2, v2), , (xn, vn) where the same key may appear many times. The accumulated value of key x is v(x) = &#x2211;{v1|xi = x}. For space reasons, we may not be able to maintain x(x) for every key x, but the sampled sum is easily maintained as the single value &#x2211; {vi | Sample(xi) = 1}. Here we show that when dealing with &#x03A9;-bit integers, if &#x0391; is a uniform odd &#x03A9;-bit integer and t is a uniform &#x03A9;-bit integer, then Sample(x) = [ax mod 2&#x03A9; &#x2264;t] is a distinguisher with probability 1/8. Working with standard units, that is, &#x03A9; = 8,16,32,64, we exploit that &#x03A9;-bit multiplication works modulo 2., discarding overflow automatically, and then the sampling decision is implemented by the C-code a*x<=t. Previous such samplers were much less computer friendly, e.g., The distinguisher of Naor and Naor [STOC'90] was more complicated and involved a 7-independent hash function.