Source khan academy We have a website with white background. The mean amount of time people spend on it is μ = 20 minutes We change the background to yellow and want to see if it improves user time on our site Null hypothesis H0
Alternative hypothesis Ha
We set a threshold significance level α = 0.05 Take sample of people visiting yellow website and calculate mean like sample mean, standard deviation, if the null hypothesis is true, what is the probability of getting a sample with statistics that we get? if that probability is lower than our significance level. if it is less than 0.05 (5%) then we reject the null hypothesis and say we have evidence for the alternative.
Step 3: take 100 samples from yellow page and calculate mean and sample standard deviation and calculate p-value (X_bar = 25, STD) Step 4: p-value: p(sample mean X_bar >= 25 | H0 is true) Step 5: if p-value < α => reject null hypothesis if p-value >= α we do not reject the null hypothesis. Hypothesis testing and p-values | Inferential statistics | Probability and Statistics | Khan Academy A neurologist is testing the effects of a drug on rat response time, by injecting 100 rats with a unit dose of the drug. Neurologist knows that
H0: drug has no effect: μ is 1.2 even with drug Ha: drug has an effect: μ is not 1.2 when drug is given Should we accept alternative hypothesis or stick with null hypothesis? Approach:
Chi-square goodness-of-fit tests Khan academy: very good! X^2 = sigma (observed - expected)^2 / expected degree of freedom = number of options - 1 then look up table to see for which degree of freedom what is the prob of this particular X^2. That becomes your p-value The McNemar’s test operates upon a contingency table. of two classifiers working on same dataset. So you get two results per sample AB test confidence online Google udacity AB testing https://www.udacity.com/course/ab-testing--ud257 |