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The T-Test

The T-Test
« PreviousHomeNext » The t-test assesses whether the means of two groups are statistically different from each other. This analysis is appropriate whenever you want to compare the means of two groups, and especially appropriate as the analysis for the posttest-only two-group randomized experimental design. Figure 1 shows the distributions for the treated (blue) and control (green) groups in a study. Actually, the figure shows the idealized distribution -- the actual distribution would usually be depicted with a histogram or bar graph. The figure indicates where the control and treatment group means are located. What does it mean to say that the averages for two groups are statistically different? This leads us to a very important conclusion: when we are looking at the differences between scores for two groups, we have to judge the difference between their means relative to the spread or variability of their scores. The formula for the t-test is a ratio. Copyright �2006, William M.K.

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Independent Groups t-test: Chapter 10 Independent Groups t-test: Chapter 10 Strength of the Relationship (10.3) - For the example we computed in class, we found a statistically significant difference – there is a “statistically significant relationship” between test-taking strategy (fake v. honest) and personality test scores. Connecting the Dots: From Christmas Cookies to Climate Just before Christmas 2014, MongaBay, one of the most widely read and well-respected forest-climate online publications, released an editorial by ADP founder Jeff Horowitz elucidating how the groceries we buy contribute to the destruction of forests. Actor/Conservationist Harrison Ford doing a bit of field research in a local supermarket. (Scene from Showtime’s Emmy Award wining climate change series “Years of Living Dangerously”) Harrison Ford checking out everyday products to see which ones contain palm oil? Not exactly the high-octane activity we associate with an adventurer like Ford…so what gives? Ford cares about the ingredients used to make these products because our high demand for these everyday consumer goods is directly tied to high rates of deforestation and climate change.

Standard deviations and standard errors Douglas G Altman (, professor of statistics in medicine1, J Martin Bland, professor of health statistics2 Author Affiliations Correspondence to: Prof Altman The terms “standard error” and “standard deviation” are often confused.1 The contrast between these two terms reflects the important distinction between data description and inference, one that all researchers should appreciate. The standard deviation (often SD) is a measure of variability. Frogs that Thrive and Dive in Vernal Pools : The National Wildlife Federation Blog In honor of Save the Frogs Day, April 30th, we’re celebrating species of frogs that depend on a very unique habitat – vernal pools. Vernal pools are shallow depressional wetlands that appear seasonally in meadows and woodlands and serve as important breeding grounds for amphibians like frogs. The seasonal wetlands and pools of the prairie pothole region are also vernal pools.

The standard error of the mean We saw with the sampling distribution of the mean that every sample we take to estimate the unknown population parameter will overestimate or underestimate the mean by some amount. But what's interesting is that the distribution of all these sample means will itself be normally distributed, even if the population is not normally distributed. The central limit theorem states that the mean of the sampling distribution of the mean will be the unknown population mean. The standard deviation of the sampling distribution of the mean is called the standard error.

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t Test We are called on many times to determine if the mean performance of two groups are significantly different. Those two groups might be students, cattle, plants, or other objects. When attempting to determine if the difference between two means is greater than that expected from chance, the "t" test may be the needed statistical technique. If the data is from a normal population and at least ordinal in nature, then we are surer that this is the technique to use. If you wish to generalize to a population, then the samples must be representative. North Temperate Lakes Background on Limnology What is limnology? Limnology is often defined as the study of inland lakes, streams, rivers and wetlands (see Stanley Dodson's Introduction to Limnology for more background on the history of limnology.) E. A. Birge, from the University of Wisconsin-Madison is one of the earliest pioneers of the science of limnology.

One-Sample T-Test - QMSS It is perhaps easiest to demonstrate the ideas and methods of the one-sample t-test by working through an example. To reiterate, the one-sample t-test compares the mean score of a sample to a known value, usually the population mean (the average for the outcome of some population of interest). The basic idea of the test is a comparison of the average of the sample (observed average) and the population (expected average), with an adjustment for the number of cases in the sample and the standard deviation of the average.