An N of 1 trial is a clinical trial in which a single patient is the entire trial, a single case study. A trial in which random allocation can be used to determine the order in which an experimental and a control intervention are given to a patient is an N of 1 randomized controlled trial. The order of experimental and control interventions can also be fixed by the researcher. This type of study has enabled practitioners to achieve experimental progress without the overwhelming work of designing a . It can be very effective in confirming causality. This can be achieved in many ways. One of the most common procedures is the ABA withdrawal experimental design, where the patient problem is measured before a treatment is introduced (baseline) and then measured again during the treatment and finally when the treatment has terminated. If the problem vanished during the treatment it can be established that the treatment was effective. But the N=1 study can also be executed in an AB quasi experimental way; this means that causality cannot be definitively demonstrated. Another variation is non-concurrent experimental design where different points in time are compared with one another. This experimental design also has a problem with causality, whereby statistical significance under a frequentist paradigm may be un-interpretable but other methods, such as clinical significance or Bayesian methods should be considered. Many consider this framework to be a proof of concept or hypothesis generating process to inform subsequent, larger clinical trials. (Wikipedia).
Testing and Online Experimentation
Join Data Science Dojo and Statsig for a conversation on experimentation and testing. Learn how leading companies like Facebook use experimentation to build better products and accelerate their growth with 10x as much testing. Web experimentation can range from simple projects like design
From playlist A/B Testing & Beyond
From playlist Open Q&A
Execution and write-up of a hypothesis test of means [in accordance with AP Statistics requirements]
From playlist Unit 8: Hypothesis Tests & Confidence Intervals for Single Means & for Single Proportions
Let Your Users Decide What They Want (Power of A/B Tests)
My talk is about letting the users help you understand what they need and if a particular feature is really useful for the users by using the technique of A/B testing. We are constantly running A/B tests to improve the customer experience by measuring the business metrics. Attend my talk t
From playlist Performance and Testing
t Test Write Up of a Hypothesis Test of an Unknown Population Mean
How to perform and write up a hypothesis test [t test] of an unknown population mean [In accordance with AP Statistics requirements]
From playlist Unit 9: t Inference and 2-Sample Inference
A Gentle Introduction to the One Sample t Test (10-2)
A one-sample t test will allow us to compare a sample mean to a population mean to determine if they are statistically significantly different. This video will introduce you to the fundamentals of the one-sample t test and prepare you for conducting one either by hand or using SPSS. This
From playlist WK10 One Sample t Tests - Online Statistics for the Flipped Classroom
Learning the One Sample t Test by Hand with Excel (10-3)
To really understand the fundamentals of statistics, it is helpful to calculate a one-sample t test by hand using formulas. To make the calculations easier, we use Excel for the math. We will use the five steps of hypothesis testing and Student's t table, to learn the test. This example us
From playlist WK10 One Sample t Tests - Online Statistics for the Flipped Classroom
Overview of the F-Test. What it is and how it works with general steps and assumptions.
From playlist F Test
L04.9 Multinomial Probabilities
MIT RES.6-012 Introduction to Probability, Spring 2018 View the complete course: https://ocw.mit.edu/RES-6-012S18 Instructor: John Tsitsiklis License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
From playlist MIT RES.6-012 Introduction to Probability, Spring 2018
12. Renewal Rewards, Stopping Trials, and Wald's Inequality
MIT 6.262 Discrete Stochastic Processes, Spring 2011 View the complete course: http://ocw.mit.edu/6-262S11 Instructor: Robert Gallager License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.262 Discrete Stochastic Processes, Spring 2011
9 4 Analysis of Contraction Algorithm 30 min
From playlist Algorithms 1
Introduction to Probability and Statistics 131A. Lecture 3. Random Variables
UCI Math 131A: Introduction to Probability and Statistics (Summer 2013) Lec 03. Introduction to Probability and Statistics: Random Variables View the complete course: http://ocw.uci.edu/courses/math_131a_introduction_to_probability_and_statistics.html Instructor: Michael C. Cranston, Ph.D.
From playlist Math 131A: Introduction to Probability and Statistics
Excel 2013 Statistical Analysis #33: Binomial Probability Distributions: Tables, Charts, Functions
Download files (which file shown at begin of video): https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch05/Ch05.htm Topics in this video: 1. (00:11) Discussion about Binominal Experiments and Probability Distributions 2. (11:00)Example 1: Experiment is attempting four sales call
From playlist Excel for Statistical Analysis in Business & Economics Free Course at YouTube (75 Videos)
A Gentle Introduction to the One Sample z Test (9-7)
Now it is time for our first real inference test (i.e. “hypothesis test”). We will use a one-sample z test to determine whether a sample mean is significantly different than the population mean when the standard deviation of the population is known. I use an example about the average age o
From playlist WK9 Using z Scores and the z Test in Statistics - Online Statistics for the Flipped Classroom
Excel Statistical Analysis 27: Binomial Probability Distributions. BINOM.DIST.RANGE Function & Chart
Download Excel File: https://excelisfun.net/files/Ch05-ESA.xlsm PDF notes file: https://excelisfun.net/files/Ch05-ESA.pdf Learn about: Topics: 1. (00:00) Introduction 2. (00:29) First look at binomial probability math formula. 3. (01:12) Requirements for a Binomial Experiment. 4. (04:46) W
From playlist Excel Statistical Analysis for Business Class Playlist of Videos from excelisfun
Chapter 5.2: Binomial Probability Distributions.
Chapter 5.2 from "Introduction to Statistics, Think & Do" by Scott Stevens (http://www.StevensStats.com) Textbook from Publisher, $29.95 print, $9.95 PDF http://www.centerofmathematics.com/wwcomstore/index.php/thinkdov4-1.html Textbook from Amazon: https://amzn.to/2zJRCjL
From playlist Statistics Lecture Videos
How to Find a Random Point in a High Dimensional Ball #SoME2
My video for the SoME2 competition hosted by 3Blue1Brown. References: - Justin's video: "The BEST Way to Find a Random Point in a Circle" (https://www.youtube.com/watch?v=4y_nmpv-9lI&list=PLnQX-jgAF5pTkwtUuVpqS5tuWmJ-6ZM-Z&index=6&t=3s) - "Vector Calculus, Linear Algebra, and Differential
From playlist Summer of Math Exposition 2 videos
Binomial Setting & Binomial Distribution in Statistics Pt 1
In a two part video I introduce the Binomial Setting and Distribution where x is defined as the number of successes. This lecture also includes the formulas and examples for of the Binomial Coefficient and the Binomial Probability Formula. I also go over how to use the binompdf(n,p,k) an
From playlist AP Statistics
13. Little, M/G/1, Ensemble Averages
MIT 6.262 Discrete Stochastic Processes, Spring 2011 View the complete course: http://ocw.mit.edu/6-262S11 Instructor: Robert Gallager License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.262 Discrete Stochastic Processes, Spring 2011
Statistics Lecture 8.2: An Introduction to Hypothesis Testing
https://www.patreon.com/ProfessorLeonard Statistics Lecture 8.2: An Introduction to Hypothesis Testing
From playlist Statistics (Full Length Videos)