GraphPad Prism combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization.
GraphPad Prism was originally designed for experimental biologists in medical schools and drug companies, especially those in pharmacology and physiology. Prism is now used much more broadly by all kinds of biologists, as well as social and physical scientists. More than 200,000 scientists in over 110 countries rely on Prism to analyze, graph and present their scientific data. It is also widely used by undergraduate and graduate students.
What makes Prism truly unique, however, is not just what it does, but how it does it. Designed for laboratory and clinical researchers, Prism doesn't expect you to be a statistician. It guides you through the analysis process – giving you as much help as you need – and tracks and organises your work like no other program available. You can concentrate on your data, not on figuring out how to use the program. Here are some of the highlights.
What's new in Prism 8?
Prism 8 introduces brand new ways to organize, analyze, and graph your data, all while making the process easier and more intuitive. Powerful new analyses and graph styles provide more ways than ever to process and present your data, while familiar features have received comprehensive improvements to enhance and streamline your work. Despite all the changes, Prism is still Prism so you can be productive right away. See highlights below:
Mixed effects model for analysis of repeated measures ANOVA with missing data
Multiple Variables Data Table for Multiple Linear Regression Analysis
Nested Data Table for Nested t test and Nested Oneway ANOVA (Mixed Effects Model)
Violin Plots for data distribution visualization
Grouped graphs that show both bars and individual points
Draw lines or brackets with centered text. Ideal for placing significance asterisks
More normality tests and plotting
More comprehensive options for one, two and threeway ANOVA
Graph customization and annotation options and much more
Statistical Features
Statistical comparisons

Paired or unpaired t tests. Reports P values and confidence intervals.

Nonparametric MannWhitney test, including confidence interval of difference of medians.

KolmogorovSmirnov test to compare two groups.

Wilcoxon test with confidence interval of median.

Perform many t tests at once, using False Discover Rate (or Bonferroni multiple comparisons) to choose which comparisons are discoveries to study further.

Ordinary or repeated measures oneway ANOVA followed by the Tukey, NewmanKeuls, Dunnett, Bonferroni or HolmSidak multiple comparison tests, the posttest for trend, or Fisher’s Least Significant tests.

Many multiple comparisons test are accompanied by confidence intervals and multiplicity adjusted P values.

GreenhouseGeisser correction so repeated measures oneway ANOVA does not have to assume sphericity. When this is chosen, multiple comparison tests also do not assume sphericity.

KruskalWallis or Friedman nonparametric oneway ANOVA with Dunn's post test.

Fisher's exact test or the chisquare test. Calculate the relative risk and odds ratio with confidence intervals.

Twoway ANOVA, even with missing values with some post tests.

Twoway ANOVA, with repeated measures in one or both factors. Tukey, NewmanKeuls, Dunnett, Bonferron, HolmSidak, or Fishers LSD multiple comparisons testing main and simple effects.

Threeway ANOVA (limited to two levels in two of the factors, and any number of levels in the third).

KaplanMeier survival analysis. Compare curves with the logrank test (including test for trend).
Column statistics

Calculate min, max, quartiles, mean, SD, SEM, CI, CV,

Mean or geometric mean with confidence intervals.

Frequency distributions (bin to histogram), including cumulative histograms.

Normality testing by three methods.

One sample t test or Wilcoxon test to compare the column mean (or median) with a theoretical value.

Skewness and Kurtosis.

Identify outliers using Grubbs or ROUT method.
Linear regression and correlation

Calculate slope and intercept with confidence intervals.

Force the regression line through a specified point.

Fit to replicate Y values or mean Y.

Test for departure from linearity with a runs test.

Calculate and graph residuals.

Compare slopes and intercepts of two or more regression lines.

Interpolate new points along the standard curve.

Pearson or Spearman (nonparametric) correlation.

Analyze a stack of P values, using Bonferroni multiple comparisons or the FDR approach to identify "significant" findings or discoveries.
Nonlinear regression

Fit one of our 105 builtin equations, or enter your own.

Enter differential or implicit equations.

Enter different equations for different data sets.

Global nonlinear regression – share parameters between data sets.

Robust nonlinear regression.

Automatic outlier identification or elimination.

Compare models using extra sumofsquares F test or AICc.

Compare parameters between data sets.

Apply constraints.

Differentially weight points by several methods and assess how well your weighting method worked.

Accept automatic initial estimated values or enter your own.

Automatically graph curve over specified range of X values.

Quantify precision of fits with SE or CI of parameters. Confidence intervals can be symmetrical (as is traditional) or asymmetrical (which is more accurate).

Quantify symmetry of imprecision with Hougaard’s skewness.

Plot confidence or prediction bands.

Test normality of residuals.

Runs or replicates test of adequacy of model.

Report the covariance matrix or set of dependencies.

Easily interpolate points from the best fit curve.
Clinical (diagnostic) lab statistics
Simulations

Simulate XY, Column or Contingency tables.

Repeat analyses of simulated data as a MonteCarlo analysis.

Plot functions from equations you select or enter and parameter values you choose.
Other calculations

Area under the curve, with confidence interval.

Transform data.

Normalize.

Identify outliers.

Normality tests.

Transpose tables.

Subtract baseline (and combine columns).

Compute each value as a fraction of its row, column or grand total.
Why Choose Prism?
Nonlinear regression
Nonlinear regression is an important tool in analyzing data, but is often more difficult than it needs to be. No other program simplifies curve fitting like Prism. In fact, you can usually fit curves in a single step. Just select an equation from the extensive list of commonly used equations (or enter your own equation) and Prism does the rest automatically  fits the curve, displays the results as a table, draws the curve on the graph, and interpolates unknown values.
Place data for multiple data sets sidebyside on an organized data table, and Prism can fit them all the data sets at once. You can fit the same model separately to each data set, use global nonlinear regression to share parameter values among data sets, or fit different models to different data sets.
Don't be fooled by the simplicity. Prism also gives you many advanced fitting options. It can report the confidence intervals of the bestfit parameters as asymmetrical ranges (profile likelihood method), which are far more accurate than the usual symmetrical intervals. It can also automatically interpolate unknown values from a standard curve (i.e., to analyze RIA data), compare the fits of two equations using an F test or Akaike's Information Criterion (AIC), plot residuals, identify outliers, differentially weight data points, test residuals for normality, and much more.
Understandable statistics
While it won't replace a heavyduty statistics program, Prism lets you easily perform basic statistical tests commonly used by laboratory and clinical researchers. Prism offers t tests, nonparametric comparisons, one, two and threeway ANOVA, analysis of contingency tables, and survival analysis. Analysis choices are presented in clear language that avoids unnecessary statistical jargon.
Unlike other programs, Prism provides understandable statistical help when you need it. Press "Learn" from any data analysis dialog and Prism's online documentation will explain the principles of the analysis to help you make appropriate choices. Once you've made your choices, Prism presents the results on organized, easytofollow tables. The Prism documentation goes beyond anything you would expect. More than half of it is devoted to thorough explanations of basic statistics and nonlinear curve fitting, to teach you what you need to know to appropriately analyze your data.
Analysis checklists
Once you’ve completed the analysis, Prism’s unique analysis checklists help you make sure you chose an analysis appropriate for your experimental design, and that you understand the assumptions behind the analysis.
Retrace every analysis
It isn’t really science unless you can document exactly how your data were analyzed. WIth Prism this isn’t a problem. You’ll never wonder how the results got there. Even if the work was done by someone else, you can review (and change) all analysis choices, and see the sequence of analyses (i.e. that the X values were transformed to logarithms before the curve was fit). Similarly, you can easily check if error bars represent the SD or SEM (or something else).
Automation without programming
All parts of your Prism project are linked. This means that when you fix a data entry error, Prism automatically updates all results, graphs, and layouts. Another advantage is that you can instantly analyze a repeat experiment. After you’ve polished the analysis and graphing steps with data from one experiment, you don’t have to repeat all those steps. Prism provides several ways to recycle your work  to instantly analyze and graph a repeat experiment, without repeating any tedious steps and without requiring any scripting or programming. Prism also offers a scripting language for those who want more complex automation.
Automatic error bars
If you enter raw data, Prism can automatically plot error bars as SD, SEM, range, interquartile range, or 95% confidence interval.
If you computed error values elsewhere, enter them into labeled (SD or SEM) subcolumns. Prism’s analyses (i.e. t test, ANOVA, regression) will take into account the SD (or SEM) and sample size.
A complete record of your work
A Prism project file can contain more than data, analyses and graphs. A Prism file can be a complete record of your experiments. Each file can contain any number of data tables, info pages, analysis results, graphs, and page layouts.
Data tables can contain not only the data you analyze and graph, but also the data you decided to exclude, which remain on the data table (in blue italics). You can highlight values you want to review later.
Each info page can store both unstructured notes and structured info constants such as lot numbers, concentrations, notebook page numbers, etc.
Graphs and layouts can be embellished with text, lines, arrows, boxes, tables, equations, pictures and more.
Annotate any page with colorcoded floating notes, and highlight (in a color you choose) the names of pages you want to look at again, or want someone else to review.
Save Prism files on your computer or network, or save directly to LabArchives, a cloudbased laboratory notebook.
Ready to publish
Prism takes you from raw data to a graph (or layout) ready for presentations, posters or publications. A single click sends completed graphs or layouts to PowerPoint or Word. Copy and paste works too, of course. Export to TIFF, EPS, JPG, or PDF (and more), with all the options you need to satisfy the requirements of scientific journals.