Take part in this free, hands-on workshop that will introduce participants to the creative and technical basics of photographic storytelling.

This session will introduce participants to non-parametric tests, which are useful when data distributions do not meet the assumptions of parametric tests. Attendees will learn to compare the adaptability of these tests with different data distributions and to visualize their operation.

By the end of the session, participants should be able to choose and apply the appropriate non-parametric tests for their data, visualize the operation of these tests, and understand the challenges of multiple testing with non-parametric methods.

This workshop will show how to interpret GAMs and how to use GAMs to make publication-level figures.

By the end of this session, participants should be able to interpret GAMs and the output of the summary() function, predict from GAMs, and make figures using GAMs.

Unlock the secrets of crafting impactful literature reviews in this interactive online workshop for graduate students. Discover how to synthesize existing research succinctly while maintaining your own critical perspective and voice.

Using accessible platforms like Google Colab, participants will learn to apply AI tools that enhance diagnostic accuracy, personalize treatments, and ultimately improve patient outcomes. This is a must-attend session for healthcare professionals and data scientists eager to address the growing demand for innovative healthcare solutions through AI.

This session will introduce participants to the Analysis of Variance (ANOVA), a statistical method used for comparing the means of three or more groups. The concept of blocking will also be introduced to reduce noise and isolate sources of variation.

By the end of the session, participants should be able to use ANOVA for multi-treatment analysis, implement blocking in experimental design, calculate the F statistic for assessing significance, and appreciate how blocking can improve the efficiency of a study.

This workshop will introduce key ideas using discipline-agnostic examples and common language while illustrating how to view the world from a Bayesian perspective.

This workshop will re-visit random and fixed effects with Hierachical GAMs (HGAMs) and expand the concepts of random slopes by introducing random smooths. The workshop will also cover smooth, nonlinear interaction terms via the ti() and te() functions.

By the end of this session, participants should be able to fit HGAMs with smooth interaction terms, plot and interpret the models.

Join our online workshop, where we'll unveil strategies for crafting the methods and results sections of your thesis.

This session will address the concepts of correlation, causation, and association in data. Participants will learn to differentiate between these concepts and to recognize and interpret various types of correlations.

By the end of the session, participants should be able to distinguish between correlation and causation, recognize the impact of confounding variables on associations, evaluate correlation reliability, and understand the significance of correlation results.

This workshop will introduce key ideas using clear mathematical notation while relating the concepts to the larger picture of Bayesian statistical inference and the Bayesian perspective of randomness, states of knowledge, and epistemology.

Ever found it challenging to answer the critical “So what” question in your research? Struggle no more!

Learn all about the publishing process, get some tips for selecting the right journal for you, and develop your skills for responding to peer reviewers at this online workshop.