--- a
+++ b/Semantic Features/ttestScript.m
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+%ttesting
+
+%test for significance. 1. This whole bloody process revolves around
+%negatives so its easy to get turned around in the naming or meaning. 2.
+%I'm including all 3 t-tests because you will get confused, and looking at
+%the results of all 3 options saves a lot of time remembering what method
+%does what you want. 3. Depending one how strong your results are, you might want a different one anyhow 
+
+%We want to do a one tail test. We only care if the ws is better or equal
+%to rt (for now) vs ws being worse than rt. Therefore null hypothesis will
+%be that wsAcc >= rtAcc (> comes from the one tail and exclusion).
+%Alternative hypothesis will be wsAcc < rtAcc 
+%'tail', 'left' Set the alternative hypothesis to be that the population mean of x is less than the population mean of y.
+%make x = wsAcc and y = rtAcc
+%Accepting the null hypothesis is success (Answer = 0. p >= 0.05)
+%Rejecting the null hypothesis is failure (Meaning that wsAcc < rtAcc)
+
+
+for i = 1:numCategories
+    pwsMultiEnsNotGreater(i) =  isNotGreaterTTest(wsMultiEnsPred(:,i), rtEnsPred(:,i), Yaverage(:,i), Yaverage(:,i)); %low p means greater
+    
+    pwsMultiEnsNotLess(i) = isNotLessTTest(wsMultiEnsPred(:,i), rtEnsPred(:,i), Yaverage(:,i), Yaverage(:,i)); %low p means less than, high P means not less than (possibly equal)
+    pwsMultiEnsNotEqual(i) = isNotSameTTest(wsMultiEnsPred(:,i), rtEnsPred(:,i), Yaverage(:,i), Yaverage(:,i)); %low p means unequal, high p means they're the same
+end
+
+
+wsMultiEnsImprovement = wsMultiEnsSuccess - rtEnsSuccess;
+[wsMultiEnsImprovement;pwsMultiEns;pwsMultiEns<0.05];
+
+%T test notes:
+%Three types of t tests
+%One Sample - If we already have a known (Iron Clad) value from other experiments and
+%want to know if this experiment produced a differing result. - Nope
+%Paired Two Sample - if you are testing the same guys but after changing
+%something. Is this us? Same photos, different processing.
+%Independant Two Sample - Probably us?
+%a = Yes or no. uselsess
+%b = probability they are the same. Lower is better. Less that 0.05 is good
+%c = Confidence Interval. 
+%d = tstat, the raw T value. df Degrees of Freedom. sd - standar deviation.