1. Hypothesis
   1. Plan
      1. The hypothesis is formed from what we want to know, understand, study
         1. Describe
         2. Prioritize
         3. Backlog
   2. Use Pattern
      1. If....., Then....
      2. SMART
      3. use Experiment description template
         1. Hypothesis. Description
            1. what and how we want to achieve
         2. Product
            1. What we do in the product
            2. what the experience of the test and control groups looks like
         3. Users
            1. which users and at what point they become a part of the experiment
            2. Minimum sample size
            3. use calc
            4. How
            5. 1
            6. Statistical Power (1−β)
            7. Determine the power of the test
            8. Percent of the time the minimum effect size will be detected, assuming it exists
            9. The power is usually set at 80% and the confidence level is 95%
            10. Significance level α
            11. Percent of the time a difference will be detected, assuming one does NOT exist
            12. the level of trust
            13. 2
            14. Determine the minimum effect that we want to notice in the experiment (in %)
            15. conv2-conv1
            16. conv2 is the expected conversion of the test group
            17. conv1 is the conversion that we have now
            18. 3
            19. Solve the equation by substituting the values ​​into the formula and look for X
            20. X is the required minimum sample size for each group
            21. x = conv2 - conv1 +-1.96 * sqrt( (conv1 * (1-conv1) / x + (conv2 * (1-conv2) /x)
         4. Key metrics
            1. for evaluating the experiment
            2. what should change, how to measure it)
         5. Expected effect
            1. sample size for experiment
         6. An action plan
            1. depending on the results of the experiment (if X, then we do Y, if Z, then we do Q)
   3. Remebmer
      1. avoid obvious hypotheses
      2. facts are not hypotheses, they are current tasks
      3. think about the metric affected by the hypothesis
         1. The recipe for a good METRIC
         2. Some notes about metrics for PO
      4. think about strategy of collection and evaluation of results
2. Action
   1. Do
      1. What do we do to test the hypothesis?
         1. think about time
   2. Start the experiment
      1. measure the results
3. Data
   1. Check
      1. what metric will change affect
   2. data collection
      1. Analyzed changes
4. Insigth
   1. Act
      1. % improvement
5. ...and
   1. Belief in effect
      1. 0... 10%
   2. Difficulty in realization
      1. 1..5
6. It will be interesting
   1. https://www.iidf.ru/media/articles/lifehacks/hadi-tsikly-5-layfkhakov/
   2. http://startupkitchen.blog/carrot-quest-case-hadi-cycles-and-how-to-increase-project-development-with-their-help/
   3. HOW WE USE PDCA
   4. Some notes about A-B testing
   5. Статистика: Задача- Метод. A/B Testing. Statistical tests (Ru, En)
   6. ML продукты. Разбираем основы
   7. Some notes about Exploratory Data Analysis (Python and Pandas)
7. New Agile-project. Let's Start!
   1. https://www.xmind.net/m/k756Qn/
8. tools
   1. tools
      1. CLT for means
      2. Normal Table - z Table - Standard Normal Table - Normal Distribution Table
      3. Distribution Calculator
      4. Sample Size Calculator (Evan’s Awesome A/B Tools)
      5. jypyter notebook
         1. Jupyter Notebook для начинающих: учебник - Еще один блог веб разработчика
      6. Values of the t-distribution (two-tailed)
      7. Understanding and Interpreting Correlations - an Interactive Visualization
      8. Diagnostics for simple linear regression