# t-test

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t-test
• The most common use comparison test between two subset of data • For Example try to make hypothesis that left-hand and right-hand have no different batting average
• t-test is used to approve or decline our null hypothesis, while t-test is parametric test in our statisitic significance test.
• NULL Hypothesis, is hypothesis for our data to be asked, if both of our data is similar? or the sample acquired through data that drawn from normal distribution(same population). • By doing t-test we reduce all the comparison test to one number only, the t value
• if only contain 1 sample, compare average, to average sample 0
• if two samples, compare average 0 to average 1.  • t-value denotes how extreme our results, and probabilty it will reject out null hypothesis

• the v value denotes how many variable took into t calculations • the p value produced by t and v, will then the value that will determine our hypothesis accepted based in our null hypothesis was true
• suppose we want to know the difference batting avg left and right, giveb our null hypothesis both no different
• then if pvalue 0.5, the variance between t and v at least 5% at extreme. • pcritical acts like some kind of threshold value. and it will determine whether our null hypothesis is accepted
• this threshold value will be quite tedious if we set it manually. rather, it can be done automatically. • by specifyinhg equal-var=true, the calculations will be exactly the same as welch's t-test
• it will return a tuple, t and p value • we can do it by comparing the sample with one another
• for our null hypothesis set to true, p must be less than p critical • Basic implementation Welch's t-test with python