diff --git a/education/statistics/Hypothesis Tests.md b/education/statistics/Hypothesis Tests.md
index 57c249b..5820072 100644
--- a/education/statistics/Hypothesis Tests.md	
+++ b/education/statistics/Hypothesis Tests.md	
@@ -2,7 +2,7 @@
 ## z tests for percentages
 This test can be used if:
 - The data is a simple random sample from the population of interest
-- The sample size is large
+- The sample size is large ($>30$)
 - A qualitative variable of interest summarized by percentages
 - Can use a box with tickets of 1s and zeros to represent the population
 If an observed value is too many SEs away from the expected value, it is hard to explain by chance.
@@ -23,8 +23,13 @@ Then you can provide a conclusion based off of either the null hypothesis, or th
 This test will look very similar to a z test for percentages, it still requires that a large, random, sample was given.
 
 ## t tests for averages
-This test is used when you have a small sample size (lt 30). 
-The only major differences used with a *t* test is that you use SD+, and 
+This test is used when you have a small sample size ($<30$). 
+The only major differences used with a *t* test is that you use SD+.
+
+With a small sample size, the standard deviation will be relatively higher, so this is compensated with the $SD_+$.
+$$ SD_+ = \sqrt{\frac{size\space sample}{sample\space size}}*SD$$
+This found value is then used in all further calculations where you would normally use the $SD$ in a z score test.
+$$ t = \frac{obs_{ave} - EV_{ave}}{SE_{ave}} $$
 ## P Value
 The chance of observing at least a sample statistic, or something more extreme, if the null hypothesis is true.
 If the p-value is less than *5*%, reject the null hypothesis.