Hello everyone. I'm Jeran from Turkey and a PhD student in neuroscience. My project is to develop a gene therapy approaches for germline mutations. That's why I'm working on resonance. So during my experimental design, I asked myself how minimize I should use. Maybe, you know, or you will learn how important to decide sample size before starting or experiment. Unfortunately, I learn the importance of sample size in a hard way during my master's thesis, my master's thesis defense. And I can definitely say, and it was awful. My master project, I was testing whether or not circulating microRNA expression level can be use as a biomarker for early detection of disrupted cholesterol metabolism. So we decide the number of patients with hypercholesterolemia at healthy people to collect their blood sample and perform downstream process. According to our budgets. I realized too late that there shouldn't be an excuse for justify your sample size according to your budgets or other things, unless you connect power analysis. What is power analyses is actually, you will learn it like me perfectly when you're watching correspond videos. But I can short. They say that it is a power of testing your hypothesis to detect true effect. So I use it in my PhD project to ask the question, how many minds I should use and also having high power to testing my hypothesis. Which is two, which is my construct. Expressing pizza protein can decrease tumor incidence caused by germline mutations. And I hope this will protect feature questions in my PhD y-bar. And also it will help to publish my results in a better Journal. The last but not least, I can, it will keep and West budgets and West animal as much less as possible. I hope you enjoy learning about power analyses and using it in your research that you will see you will kill more than two birds with one stone. That's mean you will design your experiment, excluding some problem, which you will, your volt phase at the end of your project. It was really bad. And also, you will had a great chance to publish your research because you conduct your experiment perfectly. Thank you so much for listening to me.