#Paired t test jmp trial#

Histograms shows the distribution of 5K running times of 15 women who ran the race twice. Start with histograms to get a sense of the sample distributions (hint: we’re looking to see if the data looks like it could come from a normal distribution, see Chapter 13.3 Assumptions).įigure 1. Data available at end of this page or click here.īegin with description and exploration of the data. Load the data into R as an unstacked data set. 5K pace times (kph) for 15 women (20 – 29 years) ID The race was run annually on Oahu, and the data reported are the pace for the first race and the second race, which occurred a year later (Jamba Juice – Banana Man Chase, Ala Moana Beach Park, data extracted from source, (Links to an external site.)). The data are running speed or pace during 5K race held annually on Oahu for a random sample of female runners (20 – 29 years old). Here’s an example in which a measure was taken twice for the same individuals. Example: How repeatable is human performance? These other factors should be similar for each member of the pair.

The purpose of pairing in this example is to attempt to “control” for all the socioeconomic factors that might contribute to cancer but they did not want to directly measure. Each pair of communities differed with respect to the proximity to a known source of pollution: one of the pair was close to a source of pollution and one of the pair was far from a source of pollution.The researchers selects pairs of communities with similar characteristics for many socioeconomic factors.The other twin or plant or animal clone or variety can serve as a type of control by providing a normal diet or normal environment.Īnother example is a study of environmental pollution on cancer rates in many different communities.You can give one of the twins a particular diet, or the plant or animal clones or strains can be raised in a particular environment (nutrient).Siblings or Monozygotic twins or clones, strains or varieties of plants or animals can be Paired in an experiment. Each subject’s weight before the diet was “paired” with the same subject’s weight after the diet.Īnother example comes from genetics. In the Paired T-test we have two groups but the observations in these two groups are paired. Paired means that there is some relationship between one observation in the first sample and one observation in the second sample (every observation in one sample must be paired with one observation in another sample).įor example, weight change in humans before and after a change in diet could be performed as a paired analysis. The point is that samples in one group are not the same samples in the second group. A treatment (or experimental) group with a control group.Two locations where we measure plants or animals.For the two independent sample T-test the samples are unpaired: we observed one variable on some organisms from two groups. However, there is an important difference between these two types of statistical procedures. These types of statistical procedures are similar to the 2 sample independent t-test that we discussed earlier.

Repeated measures statistical tests, like the paired T-test, are needed however to analyze the data. Many experiments are designed so that subjects receive all treatments and responses are gauged against the initial values recorded on the subjects. Repeated measures designs have many advantages we discuss them further in Chapter 14.6. Note the difference between this paired ore repeated measures design and the independent sample design (see Chapter 10.1). Subjects randomly selected from population of interest, then again once recruited into one of two treatment arms: arm 1, subjects first receive the experimental treatment, then some time later the subjects receive the control treatment arm 2, subjects first receive the control treatment, then some time later the subjects receive the experimental treatment.

#Paired t test jmp trial#

In many cases, the best control is the individual.Ĭonsider now a basic experimental design, the randomized crossover trial (Fig. Interested in a new treatment for weight loss? Define a control group to compare the weight loss by a group using the new product.