The second example uses a binary grouping variable with a single column of spending data. The first example assumes that we have two numeric vectors: one with Clevelanders' spending and one with New Yorkers' spending. ![]() In the three examples shown here we’ll test the hypothesis that Clevelanders and New Yorkers spend different amounts monthly eating out. To toggle this, we use the flag var.equal=TRUE. By default, R assumes that the variances of y1 and y2 are unequal, thus defaulting to Welch's test. The general form of the test is t.test(y1, y2, paired=FALSE). The independent-samples test can take one of three forms, depending on the structure of your data and the equality of their variances. T.test(preTreat, postTreat, paired = TRUE)Īlternative hypothesis: true difference in means is not equal to 0Īgain, we see that there is a statistically significant difference in means on t = 19.7514, p-value < 2.2e-16 Independent Samples Here, we would conduct a t-test using: set.seed(2820) ![]() We can visualize this difference with a kernel density plot as: We find that the mean systolic blood pressure has decreased to 138mmHg with a standard deviation 8mmHg. We find 1000 individuals with a high systolic blood pressure (\(\bar=145\)mmHg, \(SD=9\)mmHg), we give them Procardia for a month, and then measure their blood pressure again. The test is then run using the syntax t.test(y1, y2, paired=TRUE).įor instance, let’s say that we work at a large health clinic and we’re testing a new drug, Procardia, that’s meant to reduce hypertension. To conduct a paired-samples test, we need either two vectors of data, \(y_1\) and \(y_2\), or we need one vector of data with a second that serves as a binary grouping variable. With these simulated data, we see that the current shipment of lumber has a significantly lower volume than we usually see: t = -12.2883, p-value < 2.2e-16 Paired-Samples T-Tests T.test(treeVolume, mu = 39000) # Ho: mu = 39000Īlternative hypothesis: true mean is not equal to 39000 So, for example, if we wanted to test whether the volume of a shipment of lumber was less than usual (\(\mu_0=39000\) cubic feet), we would run: set.seed(0) ![]() To conduct a one-sample t-test in R, we use the syntax t.test(y, mu = 0) where x is the name of our variable of interest and mu is set equal to the mean specified by the null hypothesis.
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