Hi there, my name is Emma and I'm a PhD student of psychiatry. And I'm briefly going to describe how a certain kind of statistical test has been very valuable to analyze the data that I'm collecting. The data is generated from a virtual navigation task that we are running for people in person using virtual reality equipment, but also online, using a link to the game that they can pull up on their own computer. So the task is programmed to generate numeric data about the performance of each person on this task. So we have different measures of each individual's accuracy. And that's the data that I am analyzing. And I'm using mixed effects models because that can take into account the fact that we have a repeated measures design. So we are taking multiple measurements and data points for each individual per condition on the task. And it also takes into account the individual as a random effect because each individual, of course, have a slightly different way of performing the task and their slopes of performance, they will each have a natural variability because everyone is different, everyone is unique. And what mixed effects models can also do is that they can take into account the random effect of the participant, but also different effects, effects. So those are the conditions of the task. So in each version we have people running at different speeds through this virtual environment. And we also manipulate the sensory cues that are available to help them navigate through it. And they're really valuable resources for mixed effects models online. And I could easily use those to write R scripts for it. I use YouTube videos and I found PDF files online that describe what it is and give examples. And I did the practice questions that they suggested. And yeah, it's been very helpful to learn and to know how to do. And I'll probably continue to use it throughout MIT PhD as I connect. Different kinds of data is still the same task. Yep, so this is something that's been very helpful for me and I hope it's helpful for you as long. Thanks.