Abstract Many simple (univariate, bivariate) and complex (multivariate) statistical techniques are used to investigate trends in biological communities and to test hypotheses about potential relationships between ecosystem components. However, the simple methods that are commonly used (regression and correlation) are prone to Type I error (rejecting a true null hypothesis) when applied repeatedly. Although multivariate methods are preferable for community analysis, their computations are mathematically demanding and the interpretation of their outputs can be challenging. We present simple community analysis (SCA), an intuitive methodology with which to test for trend and correlation at the community-level. We demonstrate SCA using fish survey and phytoplankton count data: the non-parametric test statistic, Kendall's tau, is used to determine the strength of trends in abundance in 65 species of fish sampled during the Irish groundfish survey (maximal length of sampling periods were 1999–2007 in ICES division VIIg and 2002–2007 in divisions VIa, VIIb and VIIj, however catches of numerous species were not recorded prior to 2003) and in 77 genera of phytoplankton sampled at Irish aquaculture sites (1991–2002). The sample distribution of the test statistic (tau is used here, but other measures may be used) is compared to the expected distribution using distributional tests (Kolmogorov–Smirnov) to evaluate the significance of community-level trends. The phytoplankton community has been increasing in abundance on Irish western and southwestern coasts (Kolmogorov–Smirnov test D > 0.5, p < 0.001). Similarly, and in agreement with previously published long-term studies, Lusitanian fish have been increasing on the shelf to the north and west of Ireland ( D ≥ 0.35, p < 0.001), while the boreal community has been declining to the south (southeast, D = 0.47, p < 0.001; southwest, D = 0.32, p = 0.03). Although SCA cannot identify causality, the trends in fish communities are as expected given the combined impacts of climate change and fishing: thus, we suggest that these are currently the main drivers of change and the precise mechanisms at play merit further study with long-term data. Biological processes at aquaculture sites should be investigated further as possible mechanisms explaining both the observed positive trends in the phytoplankton community and the restriction of negative trends to Heterosigma and eight dinoflagellate genera. Applied in conjunction with other statistical tools, SCA should aid researchers who aim to describe change in communities and community-level relationships with covariates. SCA is a powerful tool for hypothesis testing at the community-level, which simultaneously produces information at the community member level for more detailed insight, while providing simple summary statistics for managers and policy makers.