Next ‐ generation sequencing has revolutionised the way we performed genomic research. Like other high throughput technologies the data generated is subject to technological and biological biases as well as systematic errors. Being aware of biases and limitations is therefore very important to minimise the impact they have on downstream analyses. In this presentation I will review common biases that are known to occur in next generation sequencing, and in some examples this will cover aspects from library prep through to data analysis. I will highlight and summarise what has been reported in the recent literature as well as showcase software tools that have been tried and tested in improving the quality of data. Finally I will focus on the quality control parameters of small RNA data sets and introduce a new software tool called miRspring and highlight what a good data set looks like.