Non-invasive hormone monitoring can offer valuable insights about reproductive status and well-being of animals, both in captivity and in the wild. One of the unique aspects of this methodology compared to traditional endocrine techniques is that it is much easier to collect repeated samples and establish longitudinal patterns of hormone fluctuations. This is valuable because the biological activity of hormones is often determined by relative changes in hormone levels, rather than absolute values. However, it can also be challenging to figure out how to analyse these large longitudinal datasets. Furthermore, excreted hormone levels can fluctuate for several reasons, thereby creating quite a bit of noise in the dataset and making it harder to separate signal from noise. This part of the workshop will focus on how to make sense of your data. We will discuss different graphing approaches and things to look for, useful summary statistics, and strategies for identifying peaks and meaningful patterns in your data. We will also highlight some confounding factors that should be taken into account during data analysis. Actual examples from the literature will be used to illustrate different data analysis approaches and some of the pros and cons of different techniques.