Statistics and Statistics with R Tutorials (All Videos) | MarinStatsLectures - Errors and Power in Hypothesis Testing | Statistics Tutorial #16 | MarinStatsLectures

Errors and Power in Hypothesis Testing and Statistics with Multiple Examples: What are Type I and Type II errors? What are false negative and false positive in hypothesis testing? What is Power(sensitivity) in Statistics? 👉🏼 Related: Power and Type II Error Calculations Video :(https://youtu.be/ryWoLqe0sd8 ); Sensitivity, Specificity, Positive and Negative Predictive Values Video (https://youtu.be/eeM7KPRNlSs) 👍🏼Best Statistics & R Programming Language Tutorials: ( https://goo.gl/4vDQzT ) ►► Like to support us? You can Donate (https://bit.ly/2CWxnP2), Share our Videos, Leave us a Comment and Give us a Like! Either way We Thank You! In this statistics video lecture we will learn about Type I Errors (False Positive), Type II Errors (False Negative), and Power (Sensitivity) in hypothesis testing. We work through multiple examples to explore the concept of errors in statistics in the context and use a 2 by 2 table to understand these concepts better. Here we will also learn how these type of errors In hypothesis testing related to each other and how the probability of making these errors can change. The terminology can be confusing at first. when "rejecting a null hypothesis", this is referred to as a "positive test result". "failing to reject the null" is a "negative test result" (much like disease testing, null is that you don’t have disease, alternative is that you do have the disease, and testing positive means we reject the null and conclude that you have the disease, and vice versa). A Type I error is when we reject the null when in reality it is true. when we reject the null this is a "positive test result" and if in reality this is incorrect, it is a "false positive". In this statistics video, we spend more time on the statistical concept and explanations and less time on calculations. ►► Watch More: ► Statistics Course for Data Science https://bit.ly/2SQOxDH ►R Course for Beginners: https://bit.ly/1A1Pixc ►Getting Started with R using R Studio (Series 1): https://bit.ly/2PkTneg ►Graphs and Descriptive Statistics in R using R Studio (Series 2): https://bit.ly/2PkTneg ►Probability distributions in R using R Studio (Series 3): https://bit.ly/2AT3wpI ►Bivariate analysis in R using R Studio (Series 4): https://bit.ly/2SXvcRi ►Linear Regression in R using R Studio (Series 5): https://bit.ly/1iytAtm ►ANOVA Statistics and ANOVA with R using R Studio : https://bit.ly/2zBwjgL ►Hypothesis Testing Videos: https://bit.ly/2Ff3J9e ►Linear Regression Statistics and Linear Regression with R : https://bit.ly/2z8fXg1 Follow MarinStatsLectures Subscribe: https://goo.gl/4vDQzT website: https://statslectures.com Facebook: https://goo.gl/qYQavS Twitter: https://goo.gl/393AQG Instagram: https://goo.gl/fdPiDn Our Team: Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC. Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH) These videos are created by #marinstatslectures to support some statistics courses at the University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials ), although we make all videos available to the everyone everywhere for free. Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn! #statistics #rprogramming