Graphpad T Test

[Pascua Gomer]

At test is used to measure the difference between exactly two means. Its focus is on the same numeric data variable rather than counts or correlations between multiple variables. If you are taking the average of a sample of measurements, t tests are the most commonly used method to evaluate that data. It is particularly useful for small samples of less than 30 observations. For example, you might compare whether systolic blood pressure differs between a control and treated group, between men and women, or any other two groups.

This calculator uses a two-sample t test, which compares two datasets to see if their means are statistically different. That is different from a one sample t test, which compares the mean of your sample to some proposed theoretical value.

Correlation and regression are used to measure how much two factors move together. While t tests are part of regression analysis, they are focused on only one factor by comparing means in different samples.

Finally, contingency tables compare counts of observations within groups rather than a calculated average. Since t tests compare means of continuous variable between groups, contingency tables use methods such as chi square instead of t tests.

Because there are several versions of t tests, it's important to check the assumptions to figure out which is best suited for your project. Here are our analysis checklists for unpaired t tests and paired t tests, which are the two most common. These (and the ultimate guide to t tests) go into detail on the basic assumptions underlying any t test:

The three different options for t tests have slightly different interpretations, but they all hinge on hypothesis testing and P values. You need to select a significance threshold for your P value (often 0.05) before doing the test.

While P values can be easy to misinterpret, they are the most commonly used method to evaluate whether there is evidence of a difference between the sample of data collected and the null hypothesis. Once you have run the correct t test, look at the resulting P value. If the test result is less than your threshold, you have enough evidence to conclude that the data are significantly different.

If the test result is larger or equal to your threshold, you cannot conclude that there is a difference. However, you cannot conclude that there was definitively no difference either. It's possible that a dataset with more observations would have resulted in a different conclusion.

Depending on the test you run, you may see other statistics that were used to calculate the P value, including the mean difference, t statistic, degrees of freedom, and standard error. The confidence interval and a review of your dataset is given as well on the results page.

This calculator does not provide a chart or graph of t tests, however, graphing is an important part of analysis because it can help explain the results of the t test and highlight any potential outliers. See our Prism guide for some graphing tips for both unpaired and paired t tests.

Prism is built for customized, publication quality graphics and charts. For t tests we recommend simply plotting the datapoints themselves and the mean, or an estimation plot. Another popular approach is to use a violin plot, like those available in Prism.

An outlier is a data point on the extreme end of your dataset. It could be very large or very small, but it is abnormally different from most of the other values in your dataset. There are many reasons for outliers, and they can show up in any kind of study.

It's best to think about outliers as points of interest, and what to do with them isn't straightforward. They could be as simple as data entry errors or the outliers could themselves be an important research finding. That's quite a range, and it could be anywhere in between, too! Use our outlier checklist to help decide what to do in your case.

Grubbs' Test, or the extreme studentized deviant (ESD) method, is a simple technique to quantify outliers in your study. It is based on a normal distribution and a test statistic (Z) that is calculated from the most extreme data point. The test statistic corresponds to a p-value that represents the likelihood of seeing that outlier assuming the underlying data is Gaussian.

The P value is interpreted like normality testing: If the P value corresponding to this Z is less than the alpha value chosen (such as .05), it is considered a significant outlier. The results page will then mark this data point as an outlier. If that P value is greater than alpha, the test concludes there is no evidence of an outlier in your dataset.

The second main limitation is that Grubbs' assumes the data was sampled from a normal (or gaussian) distribution. However, it's rare to observe "normal" data in the world. For example, in the biological sciences, data often follows a lognormal distribution, which looks at first to have obvious outliers if the pattern is not recognized appropriately. See our example that uses Grubbs' Test on a lognormal distribution.

Outliers lend themselves to graphics perhaps more than any other aspect of statistics. Scatter plots, box plots, and violin plots are common ways to see where your dataset clumps together and which values are the extremes.

Enjoying this calculator? Prism offers more capabilities for outlier detection, including methods like Grubbs' Test, ROUT, and more. We offer a free 30-day trial of Prism and its publication-ready graphic creation.

Contingency tables are used to analyze count data across two or more experimental factors by separating the subjects into the appropriate categories. An example is comparing subjects with and without some risk factor (such as smoking/non-smoking) and further categorizing by whether they have a disease (such as lung cancer).

Unlike regression analysis or ANOVA, both of the factors are categorical (rather than numeric variables). A 2x2 table means that subjects are separated based on two factors (or questions) with two levels in each factor (groups 1 or 2 for the first factor and outcome 1 or 2 for the second factor). Each subject falls into one of the two levels for each factor, which results in four possible categories in all.

The goal is to determine if the factors are associated, for example, a subject in group 1 may be more likely to be part of the outcome 2 category. Be careful with interpretation, though, as a relationship does not necessarily imply causation!

Suppose you recruit a fixed number of people with and without lung cancer. Then you interview each subject and record whether they are smokers or not. Notice these are both factors with exactly two possibilities.

This study would correspond to a contingency table like the one below, where you could count the number of subjects in each of the four categories. Testing the differences between the observed and expected counts can help you quantify the relationship between smoking and lung cancer.

Chi-square tests compare the observed (O) and expected (E) frequencies of the subjects. With contingency table tests, the expected frequencies are calculated in the background based on the multiplication rule of probability. The idea is to use the row and column (marginal) totals to calculate the expected counts if there is no association between the variables. If the observed values vary significantly from the expected values (using a chi-square test), then there is statistical evidence of association.

Several methods exist to calculate Fisher's test, and this calculator uses the summing small P values method. Fisher's test is rarely calculated by hand and can be very intensive even for a computer.

Statistical tests for contingency tables evaluate whether the factors are associated. After you click calculate, the P value will be reported along with a sentence describing its statistical significance. For chi-square this will also include the chi-square test statistic and its degrees of freedom.

Although this calculator does not create a graphic of the relationship between the groups and outcomes, you might want to look at a grouped bar chart that compares your observed and expected counts. That will visually show you which categories vary from what would be expected if there was no association between the variables.

The sign test is a special case of the binomial case where your theory is that the two outcomes have equal probabilities. Number of "successes" you observed = Number of trials or experiments = You will compare those observed results to hypothetical results. What is the hypothetical probability of "success" in each trial or subject? (For a sign test, enter 0.5.) Probability = Calculate Probabilities Clear The Form Analyze, graph and present your scientific work easily with GraphPad Prism. No coding required.

I have three sets of data, to which I want to apply Dunn's test. However, the test shows different results when performed in GraphPad Prism and R. I've been reading a little bit about the test here, but I couldn't understand why there is a difference in the p-values. I even tested in R all the methods to adjust the p-value, but none of them matched the GrapPad Prism result.

In Prism, you specified that each row (data for each day) is a matched set, so a repeated measures analysis is performed. Because you specified a nonparametric test, it does the Friedman test with Dunn followup comparisons.

I am not very familiar with those R commands, but it doesn't look like you specified pairing or repeated measures. I think your R analysis is doing the Kruskal-Wallis nonparametric test (without pairing) with Dunn followup comparisons.

"Dunn's" test just means it corrects for multiple comparisons using what is often called the Bonferroni method (but it is more appropriate to attribute to Dunn). Dunn's adjustment can be done for many kinds of analyses, including repeated measures (paired) or not.

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