#### What a t-test easily hides

Currently Under Review.

To justify the effort of developing a theoretical construct, a theoretician needs empirical data that support a non-random effect of sufficiently high replication-probability. To establish these effects statistically, researchers (rightly) rely on a *t*-test. But many pursue questionable strategies that lower the cost of data-collection. Our paper reconstructs two such strategies. Both reduce the minimum sample-size (N_{MIN}) sufficing under conventional errors (*α*, *β*) to register a given effect-size (*d*) as a statistically significant non-random data signature. The first strategy increases the *β*-error; the second treats the control-group as a constant, thereby collapsing a two-sample *t*-test into its one-sample version. (A two-sample *t*-test for *d*=0.50 under a*=β*=0.05 with N_{MIN}=176, for instance, becomes a one-sample *t*-test under a*=*0.05, *β*=0.20 with N_{MIN}=27.) Not only does this decrease the replication-probability of data from (1-*β*)=0.95 to (1-*β*)=0.80, particularly the second strategy cannot corroborate hypotheses meaningfully. The ubiquity of both strategies arguably makes them partial causes of the confidence-crisis. But as resource-pooling would allow research groups reach N_{MIN} jointly, a group’s individually limited resources justify neither strategy.