A recent analysis revealed that most environmental microbiologists neglect replication in their science (Prosser, 2010). Of all peer-reviewed papers published during 2009 in the field's leading journals, slightly more than 70% lacked replication when it came to analyzing microbial community data. The paucity of replication is viewed as an ‘endemic’ and ‘embarrassing’ problem that amounts to ‘bad science’, or worse yet, as the title suggests, lying (Prosser, 2010). Although replication is an important component of experimental design, it is possible to do good science without replication. There are various quantitative techniques – some old, some new – that, when used properly, will allow environmental microbiologists to make strong statistical conclusions from experimental and comparative data. Here, I provide examples where unreplicated data can be used to test hypotheses and yield novel information in a statistically robust manner.