Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions
1) Strength of association (how strong is the association between the data?) 2) Consistency (will the same results be found if different people replicate the study at different times?) 3) Specificity (how specific is the association?) 4) Temporality (did the effect occur after the cause?) 5) Biological gradient (is there a correlation between the a
... See moreAlbert Rutherford • Statistics for the Rest of Us: Mastering the Art of Understanding Data Without Math Skills (Advanced Thinking Skills Book 4)
The null hypothesis in probability and statistics is the starting assumption that nothing other than random chance is operating to create the observed effect that you see in a particular set of data.
Maura Ginty • Landing Page Optimization: The Definitive Guide to Testing and Tuning for Conversions
With null hypothesis testing, all it takes is sufficient evidence (instead of definitive proof) that a 0 difference between means isn’t likely and you can operate as if at least some difference is true.
Jeff Sauro • Quantifying the User Experience: Practical Statistics for User Research
Frequentist inference begins by assuming a null hypothesis to be true before data are collected (eg, that there is no effect of a particular treatment on survival). Investigators then collect data, analyze them, and ask, “How surprising is my result if there is actually no effect of the treatment on survival?” The data would be surprising if there ... See more