Researchers occasionally have to work with an extremely small sample size, defined herein as "N" less than or equal to 5. Some methodologists have cautioned against using the "t"-test when the sample size is extremely small, whereas others have suggested that using the "t"-test is feasible in such a case. The present simulation study estimated the Type I error rate and statistical power of the one- and two-sample "t"-tests for normally distributed populations and for various distortions such as unequal sample sizes, unequal variances, the combination of unequal sample sizes and unequal variances, and a lognormal population distribution. "N"s per group were varied between 2 and 5. Results show that the "t"-test provides Type I error rates close to the 5% nominal value in most of the cases, and that acceptable power (i.e., 80%) is reached only if the effect size is very large. This study also investigated the behavior of the Welch test and a rank-transformation prior to conducting the "t"-test ("t"-testR). Compared to the regular "t"-test, the Welch test tends to reduce statistical power and the "t"-testR yields false positive rates that deviate from 5%. This study further shows that a paired "t"-test is feasible with extremely small "N"s if the within-pair correlation is high. It is concluded that there are no principal objections to using a "t"-test with "N"s as small as 2. A final cautionary note is made on the credibility of research findings when sample sizes are small. (Contains 4 tables and 4 figures.)