High-throughput quantitative “omics” experiments often involve characterisation of a large number of biomolecules. Over the years, prioritising this list for subsequent follow-up analysis is generally carried out by applying arbitrary p value and/or log fold change thresholds. Recently, statisticians worldwide have expressed their concerns over the use of p value as a surrogate for effect size (1). Additionally, filtering by fold changes also requires an expert insight to determine the threshold. As an alternative, a method of ranking biomolecules by confidence bounds on the effect size, ‘topconfects’, has been proposed recently (2). It emphasises on biggest effect size in contrast to consistent but small changes in case of p value based filtering. This method has shown promising results with ‘transcriptomics’ data highlighting the biologically significance. Here, we applied ‘topconfects’ to a label-free proteomics dataset and found marked differences in top-ranked protein list generated by confidence bounds and p values.