Graphpad Add Error Bars

[Carmina Piette]

Prismcan graph error bars either from replicates you enter, or error values (SD, SEM, etc.) you enter. But sometimes, no error bar appears for certain points on XY graphs. The reason is simple. If the error bar would be shorter than the size of the symbol, Prism simply won't draw it, even if the symbol is clear.

I can extend the range of the y axis to make sure the upper limit is included, but then it squashes the bulk of the data points together and doesn't really display the main trend of the bulk of the graph well.

The only place I can think of where "error bars" (better to use confidence limits and specify the confidence level) are out of control is where they should have been shown on the log scale but weren't. For example, if one is estimating hazard ratios, odds ratios, risk ratios, or fold-change, it is more appropriate to use a log scale when presenting the point estimates and confidence limits. This will also prevent wild limits from re-scaling the graph in way that obscures the region of interest.

We may be able to find some guidance in particular situations, but hard and fast rules are unlikely. Even if someone suggested them, we would be sure to find an exception. The only guidance I have seen about the subject is in LeLand Wilkinson's The Grammar of Graphics. Wilkinson suggests that plots of distributions, even if summarized by error bars, should always contain the full range of the data in the axis. I suspect the motivation for this should be fairly intuitive.

You give on its face a reasonable exception though (Wilkinson's suggestion was more so in the context of default behavior of graphics as opposed to any rule). Alternatives (I can think of) besides truncating the error bar is to consider separate panels in which the axis length varies between panels, or simply two plots (one zoomed out and one zoomed in). Regardless of how you handle the situation, the graphic and the text should be clearly annotated so no confusion arises.

Assuming you have a chart with just your data points being displayed,, all you have to do, is to right click on the graph, and add a second instance of Points, and set them to the mean and error bars.

The n-1 equation is used in the common situation where you are analyzing a sample of data and wish to make more general conclusions. The SD computed this way (with n-1 in the denominator) is your best guess for the value of the SD in the overall population.

The goal of science is always to generalize, so the equation with n in the denominator should not be used. The only example I can think of where it might make sense is in quantifying the variation among exam scores. But much better would be to show a scatterplot of every score, or a frequency distribution histogram.

The New England Journal of Medicine (NEJM) states: Except when one-sided tests are required by study design, such as in noninferiority trials, all reported P values should be two-sided. In general, P values larger than 0.01 should be reported to two decimal places, those between 0.01 and 0.001 to three decimal places; P values smaller than 0.001 should be reported as P

We never intended to create a style, but GraphPad (GP) programs are in wide use, so many people follow our lead. GraphPad InStat and Prism always report a zero before the decimal point, and four digits after. If the P value is less than 0.0001, we report

If each value represents a different individual, you probably want to show the variation among values. Even if each value represents a different lab experiment, it often makes sense to show the variation.

With fewer than 100 or so values, create a scatter plot that shows every value. What better way to show the variation among values than to show every value? If your data set has more than 100 or so values, a scatter plot becomes messy. Alternatives are to show a box-and-whiskers plot, a frequency distribution (histogram), or a cumulative frequency distribution.

What about plotting mean and SD? The SD does quantify variability, so this is indeed one way to graph variability. But a SD is only one value, so is a pretty limited way to show variation. A graph showing mean and SD error bar is less informative than any of the other alternatives, but takes no less space and is no easier to interpret. I see no advantage to plotting a mean and SD rather than a column scatter graph, box-and-wiskers plot, or a frequency distribution.

If your goal is to compare means with a t test or ANOVA, or to show how closely our data come to the predictions of a model, you may be more interested in showing how precisely the data define the mean than in showing the variability. In this case, the best approach is to plot the 95% confidence interval of the mean (or perhaps a 90% or 99% confidence interval).

What about the standard error of the mean (SEM)? Graphing the mean with an SEM error bars is a commonly used method to show how well you know the mean, The only advantage of SEM error bars are that they are shorter, but SEM error bars are harder to interpret than a confidence interval.

This example shows how to make an odds ratio plot, also known as a Forest plot or a meta-analysis plot, graphs odds ratios (with 95% confidence intervals) from several studies. It also shows how to place a custom grid line on a graph.

Epidemiologists often like to make the x axis logarithmic. This makes sense as it makes odd ratios greater than 1.0 and less than 1.0 symmetrical (for example, an odds ratio of 2.0 becomes symmetrical with an odds ratio of 0.5).

You can interpret the rsults of two-way ANOVA by looking at the P values, and especially at multiple comparisons. Many scientists ignore the ANOVA table. But if you are curious in the details, this page explains how the ANOVA table is calculated.

I analyzed the data four ways: assuming no repeated measures, assuming repeated measures with matched values stacked, assuming repeated measures with matched values spread across a row, and with repeated measures in both directions. The tables below are color coded to explain these designs. Each color within a table represents one subject. The colors are repeated between tables, but this means nothing.

Each mean square value is computed by dividing a sum-of-squares value by the corresponding degrees of freedom. In other words, for each row in the ANOVA table divide the SS value by the df value to compute the MS value.

Each F ratio is computed as the ratio of two MS values. Each of those MS values has a corresponding number of degrees of freedom. So the F ratio is associated with one number of degrees of freedom for the numerator and another for the denominator. Prism reports this as something like: F (1, 4) = 273.9

The potency of a drug is commonly quantified as the EC50, the concentration that leads to 50% maximal response (or the logarithm of the EC50). But in some systems you might be more interested in the EC80 or the EC90 or some other value. You can either compute these values from the EC50 or fit a curve in such a way as to directly fit ECanything.

If you know the EC50 and Hill slope (H), you can easily compute the EC80 or EC10 or any other value you want. For example, if the Hill slope equals 1, the EC90 equals the EC50 times nine. If H equals 0.5, the curve is shallower and the EC90 equals the EC50 times 81.

You can fit data directly to an equation written in terms of the ECF, where F=fraction of maximal response (example: for EC90, F=90). The advantage of this approach is that Prism will report the 95% confidence value for ECF.

When a dose-response curve is incomplete, then the determination of a value on the curve is not accurate. If you can not define the top and bottom plateaus of a curve, then it is not possible to determine other regions of the curve that are defined by their relationship to the top and bottom plateaus. This point is discussed further in a section of FAQ 1356.

As the concentration (X) goes up, the dose-response equation computes the response (Y) as getting closer and closer to the Top plateau. But it never reaches it. When a drug binds to a receptor with mass action rules, the fraction occupancy equals D/(D+K), where D is the concentration of drug (that you vary) and K is the equilibrium binding dissioction constant, which is a fixed property of the drug and receptor. As D gets higher and higher, the fractional occupancy gets closer and closer to 1.0, but never reaches it. Therefore, there can be no EC100. And no EC0.

GraphPad Software Inc. was a privately held software development corporation until its acquisition by Insight Partners in 2017.[1] The company was named Insightful Science, which itself merged with Dotmatics in 2021.[2] The original software was written by Harvey Motulsky in 1989 and it was co-founded by Motulsky and Earl Beutler. The company operates in California.[3] Its products include the 2D scientific graphing, biostatistics, curve fitting software GraphPad Prism and the free, web-based statistical calculation software, GraphPad QuickCalcs.

GraphPad Prism is a commercial scientific 2D graphing and statistics software for Windows and Mac OS desktop computers. Software features include nonlinear regression, with functionalities including the removal of outliers, comparisons of models, comparisons of curves, and interpolation of standard curves. The software allows the automatic updating of results and graphs, and has functionality for displaying error bars.[4][5] Features for usability include built in formulae, batch processing and standardisation features, along with automated analysis and data validation.[6]

In my experience, the most common mistake I see in figures from the life sciences, is that they use error bars without explaining what they mean. An error bar could be a standard deviation of the data. It could be a standard deviation of a summary statistic, a standard error, or it could be a confidence interval.

For some distributions, the error bars are equivalent to a 68% confidence interval. For example, the normal distribution for going plus or minus one standard, is 68%. There are many distributions where the confidence intervals are not based on standard error and even where they are, do you really care about a 68% confidence interval? If you think of tables, if you give a standard error, then people can multiply it by whatever they want to come out with a confidence interval. But a graph is a finished product, so they're not going to multiply and it doesn't make sense.

This was pointed out in Cleveland's book, which I consider the best book in the field. It was published in '85 and it's called The Elements of Graphing Data, and here is a page from his book. He mentions that each error bar conforming to the convention in science and technology, shows plus and minus one standard error. The interval formed by the error bars is a 68% confidence interval, which is not a particularly interesting interval. One standard error bars are a naive translation of the convention for numerical reporting of sample to sample variation.

Let's just look at a few figures I've seen. Here is perceived cancer risk. We have error bars, we have no idea what they referred to. This is a figure that I found in a book on visualization no less, where they're showing world's car production and they use these ridiculous tilted pie charts. I actually contacted an author to find out why they tilted them this way and they said, "Oh, it makes it easier to follow the orange or follow the green, from one pie to another."

What they show is, as I say, world's car production. The blue is Japan, the red is USA, etc. Well first of all, people are going to be misled because 77 is on the right and 80 on the left. By convention, we read graphs from left to right, but this goes from right to left. So people are going to be confused and I think people will have a lot of trouble with it. What I did to redraw it, was use a different panel. I fixed one of the variables, in this case the country, and then I did a plot of the other two variables. Here, I think it's much easier to see which ones are increasing and which are decreasing.

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