How to Do a One Tailed T-Test in Statistics

In this paper I am going to perform a one-tailed, one-sample t¬-test in order to test the null surmise that the modal(a) number of ounces of metric grain per stroke equals sixteen. A shaper of a particular brand of cereal maintains there is an sightly of sixteen ounces of cereal per box. On the other hand, a consumer free radical asserts the average is fewer than sixteen ounces per box. The consumer group is going to deposit a class action lawsuit for false announce if the average is fewer than sixteen ounces per box. I was hired by the consumer group to determine whether the consumer group is correct or the cereal manufacturer is correct.

According to Sprinthall (2003, p. 249), Sometimes, however, a researcher not unaccompanied assumes that a mean difference between samples will blow over simply also predicts the direction of the difference¬. When this happens, the statistical finale is not based on both tails of the distribution, but only one. In this instance, the sign of the t ratio is crucial. When conducted in this way, the t test is called a one-tail t test. The calculation of a one- tail t test is identical to that of the two-tail t. The only differences be in the way the alternative hypothesis is written and in the method use for looking up the table entertain of t [italics in original].

I randomly selected 20 supermarkets, and from separately supermarket I randomly selected one box of the manufacturers cereal. I then weighed the cereal in each box for all twenty boxes. I came up with numbers ranging from 12.4 universe the lowest to 18.4 being the highest weight of the cereal. I then used the one-tailed, one-sample t test to test the null hypothesis that the average number of ounces of cereal per box equals sixteen.

After completing the one-tailed, one-sample t test I compared the alpha level to the significance and firm to retain the null hypothesis.

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