# Recent content by obh

1. ### t, Z and sigma again

Ps if you use the Z-test with the population standard deviation, which is the correct use for the test, you get type I error similar to the significance level. (0.05) This is the expected result for any test used properly.
2. ### t, Z and sigma again

Hi Joe, Sorry if I wasn't clear when posting the attached chart, it should answer your question. The chart shows the actual type I error (rejecting correct H0) Blue Z - The actual type I error for the Z test when using the sample standard deviation. Red T - The actual type I error for the...
3. ### t, Z and sigma again

Hi Joe, I showed that when you estimate the standard deviation, the t-distribution, give better results than the normal distribution, even when n>30.
4. ### Mann Whitney Test

Hi Neal, 1. When both distributions have a similar shape you may also say it compares the medians. When both distributions are symmetrical, the median and the mean are similar. 2. Correct, the Mann Whitney U test checks the difference in the ranks. 3. I assume this says something like 2., as...
5. ### Question in interpreting Effects Size - versus p - Very large data sets

0.13 doesn't seem to be useless, but as you wrote, you can't just take every combination, you need to have theory, as randomly one of the combination may work.
6. ### Conditional probability- need to know how to identify the P(A and B) please

long Long time since I used conditional probability... The unconditional probability is P(X=10)=0.05 But your question is P(X=10 | (P(X in (7,8,9,10))
7. ### Question in interpreting Effects Size - versus p - Very large data sets

Hi JDB, I may read tomorrow the review. When you have such a huge sample size, even a minor insignificant effect may be statistically significant. In a small sample size, a small effect size may not be significant. So yes you should also look at the effect size, always, not only in huge...
8. ### t, Z and sigma again

Hi Joe, I ran the simulation over a list of samples. Even for a sample size of 30, it is better to use the t-test. Actually there is no reason to use the z-test ...
9. ### t, Z and sigma again

Hi Joe, The t-distribution has heavy tails compares to the normal distribution, and the tails are heavier for a small DF value, limit to the normal distribution tails for a large DF value. The difference between 0.05 and 0.065 is very big, as the region of rejection is in the tail.
10. ### t, Z and sigma again

if(!"BSDA" %in% installed.packages()){install.packages("BSDA")} library(BSDA) reps <- 100000 # number of simulations n1 <- 20; # sample size #population sigma1 <- 12# true SD mu1 <- 100# true mean pvalues_t <- numeric(reps) pvalues_z <- numeric(reps) set.seed(1) for (i in 1:reps) { x1 <-...
11. ### Reporting occurrances

Hi You should know what are your questions. Statistics is a "tool" to answer your question. If your question is how to present the data, than stacked bar may be a good way.
12. ### Reporting occurrances

Welcome, BenHoughton. So you only want to present the data? There are many correct ways to present data. When I need to present data I usually try several options and then choose the best for my personal opinion. You may create stacked bar char per the number of balloons, with different bar...
13. ### Can someonedy solve this? I have to submit it in one hour

You can have the results in the following calculator link You also have the formulas at the bottom of the link. It is better that you will calculate yourself and check the results with the calculator. http://www.statskingdom.com/180Anova1way.html
14. ### What statistical test would best suit my data?

Hi CMS What outcome do you want to check? I assume you compare period before removing the incentive, to period after? What timeframe do you check? If you want to compare the average of the outcome before, to the average of the outcome after, the default test is paired T-test.
15. ### Homogeneity requirements for Linear regression

Correct, for example, CLT (if relevant ...) doesn't always work, try to do CLT on data with undefined skewness like F(3,3). the sample average will be skewed also for a huge sample size.