An introductory tutorial on the Mann–Kendall Test for Trend Detection by Dr. Mrinmoy Majumder offers an invaluable starting point for anyone working with environmental and hydro‑meteorological time series. The Mann–Kendall test is one of the most widely used non‑parametric tools to detect whether a dataset shows a statistically significant increasing or decreasing trend over time, without assuming normality of the data. This makes it particularly powerful for real‑world datasets in hydrology, climate science, water resources, and environmental management, where outliers, non‑normal distributions, and missing values are common.
In this tutorial, Dr. Majumder introduces the core idea of a monotonic trend and explains why traditional linear regression is not always the best choice when data do not meet strict statistical assumptions. He walks through the logic of the Mann–Kendall test step by step: how pairwise comparisons between data points are turned into a test statistic, how the sign of this statistic indicates upward or downward trend, and how significance is assessed using p‑values and standardized Z‑scores. The explanation is conceptual rather than purely formula‑driven, which helps learners internalize why the test works, not just how to press buttons in software.
The tutorial is especially relevant for B.Tech, M.Tech, MSc, and PhD students, as well as early‑career researchers who are preparing theses, dissertations, or journal papers that require robust trend analysis. Dr. Majumder connects the method to practical applications such as rainfall and temperature trends under climate change, streamflow and groundwater decline, and long‑term water quality variation. This application‑first orientation helps viewers see how the Mann–Kendall test supports evidence‑based decision making in planning, design, and policy.
Another strength of the session is its emphasis on interpretation and reporting, which is often where students struggle the most. The tutorial clarifies how to write meaningful statements like “a statistically significant increasing trend in annual rainfall” and how to distinguish between statistical significance and practical or physical significance in a system. Learners also gain a clear sense of when to combine the Mann–Kendall test with related tools such as Sen’s slope for estimating trend magnitude, and when more advanced variants or pre‑processing (e.g., for autocorrelation or seasonality) may be necessary.
Overall, this introductory tutorial serves as a gateway to serious trend analysis in hydroinformatics and environmental data science. Viewers come away with a strong conceptual foundation, a clear workflow for applying the test to their own datasets, and the confidence to defend their methodological choices in academic and professional settings. It is an excellent resource to bookmark if you are beginning work on climate or water‑related time series and want a solid, instructor‑led introduction to the Mann–Kendall test.
