Hurlbert divides experimental ecologist into ‘those who do not see any need for dispersion (of replicated treatments and controls), and those who do recognize its importance and take whatever measures are necessary to achieve a good dose of it’. Experimental ecologists could also be divided into those who do not see any problems with sacrificing spatial and temporal scales in order to obtain replication, and those who understand that appropriate scale must always have priority over replication. If an experiment is conducted in a spatial or temporal scale, where the predictions of contesting hypotheses are convergent or ambiguous, no amount of technical impeccability can make the work instructive. Conversely, replication can always be obtained afterwards, by conducting more experiments with basically similar design in different areas and by using meta-analysis. This approach even reduces the sampling bias obtained if resources are allocated to a small number of well-replicated experiments. For a strict advocate of the hypothetico-deductive method, replication is unnecessary even as a matter of principle, unless the predicted response is so weak that random background noise is a plausible excuse for a discrepancy between predictions and results. By definition, a prediction is an ‘all-statement’, referring to all systems within a well-defined category. What applies to all must apply to any. Hence, choosing two systems and assigning them randomly to a treatment and a control is normally an adequate design for a deductive experiment. The strength of such experiments depends on the firmness of the predictions and their a priori probability of corroboration. Replication is but one of many ways of reducing this probability. Whether the experiment is replicated or not, inferential statistics should always be used, to enable the reader to judge how well the apparent patterns in samples reflect real patterns in statistical populations. The concept ‘pseudoreplication’ amounts to entirely unwarranted stigmatization of a reasonable way to test predictions referring to large-scale systems.