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The preferred analysis and graphing solution purpose-built for scientific research. Join the world’s leading scientists and discover how you can use Prism to save time, make more appropriate analysis choices, and elegantly graph and present your scientific research.

GraphPad Prism, available for both Windows and Mac computers, 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. Sign up to start your free 30 day trial! No credit card, no commitment required.

Comprehensive Analysis and Powerful Statistics, Simplified

Organize Your Data Effectively

Prism is specifically formatted for the analyses you want to run, including analysis of quantitative and categorical data. This makes it easier to enter data correctly, choose suitable analyses, and create stunning graphs.

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Perform The Right Analysis

Avoid statistical jargon. In clear language, Prism presents an extensive library of analyses from common to highly specific— t tests, one-, two- and three-way ANOVA, linear and nonlinear regression, dose-response curves, binary logistic regression, survival analysis, principal component analysis, and much more. Each analysis has a checklist to help you understand the required statistical assumptions and confirm you have selected an appropriate test.

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Get Actionable Help As You Go

Reduce the complexity of statistics. Prism’s online help goes beyond your expectations. At almost every step, access thousands of pages from the online Prism Guides. Browse the Graph Portfolio and learn how to make a wide range of graph types. Tutorial data sets also help you understand why you should perform certain analyses and how to interpret your results.

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Work Smarter, Not Harder

One-Click Regression Analysis

No other program simplifies curve fitting like Prism. Select an equation and Prism does the rest—fits the curve, displays a table of results and function parameters, draws the curve on the graph, and interpolates unknown values.

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Focus on Your Research, Not Your Software

Prism handles the coding for you. Graphs and results are automatically updated in real time. Any changes to the data and analyses— direct entry of data, omitting erroneous data, correcting typos, or changing analysis choices—are reflected in results, graphs, and layouts instantaneously.

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Automate Your Work Without Programming

Automatically add multiple pairwise comparisons to your analysis with a single click. For customization options of these lines and asterisks, simply click the toolbar button again. Make adjustments to the data or the analysis, and the results displayed on the graph will update automatically.

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The Fastest Way to Elegantly Graph and Share Your Work

Countless Ways to Customize Your Graphs

Focus on the story in your data, not manipulating your software. Prism makes it easy to create the graphs you want. Choose the type of graph, and customize any part—how the data is arranged, the style of your data points, labels, fonts, colors, and much more. The customization options are endless.

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Explore Your Data

Focus your efforts on analyzing the most relevant data. Customize how you present the relationships in the data to effectively explore large data sets.

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Export Publication-Quality Graphs With One Click

Reduce time to publish. Prism allows you to customize your exports (file type, resolution, transparency, dimensions, color space RGB/CMYK) to meet the requirements of journals. Set your defaults to save time.

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Enhance Collaboration

Share more than your graphs. Prism’s comprehensive record of your data enables effective collaboration with other scientists. All parts of your Prism project (raw data, analyses, results, graphs, and layouts) are contained in a single file that you can share with one click. Now others can easily follow your work at every step, enhancing the clarity of your findings and streamlining your collaborative efforts.

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Discover What’s New in Prism!

Introducing an even more powerful Prism featuring enhanced data visualization and graph customization, more intuitive navigation, and more sophisticated statistical analyses.

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Enhanced Data Visualization and Graphs Customization

Bubble Plots

Create Bubble Plots directly from raw data, encoding variables for position (X- and Y-coordinates), color, and size

Violin plots

Visualize distributions of large data sets with either extended or truncated violin plots

Estimation Plots

Automatically display your analysis results

Smoothing spline

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Major improvements in showing general data trends through Akima splines and smoothing splines with improved control over the number of knots, or inflection points

Improved Graphing and Customization Options

Stars on Graph

Automatically add multiple comparison results to graphs

Automatically label bar graphs

Annotate your bar graphs with values for the means, medians, or sample sizes to emphasize what's important in your work

Improved grouped graphs

Easily create graphs that show both individual points (scatter) along with bars for mean (or median) and error bars

More Intuitive Navigation

Find related sheets easily

New family panel shows the family of sheets related to the current sheet, and chains of analyses are automatically indented

Easily navigate between multiple results tables

Analyses with multiple results tables now grouped into a single sheet with tabs for each result table; choose which tabs to show or hide

Improved Search

Search by sheets with highlights or notes of specified color

Now Featuring Eight Kinds of Data Tables

Multiple variables data table

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Each row represents a different subject and each column is a different variable, allowing you to perform multiple linear regression (including Poisson regression), extract subsets of data into other table types, or select and transform subsets of the data. Prism’s multiple variable tables support text variables and have the ability to define variable types

Nested data table

Analyze and visualize data that contains subsets within related groups; Perform nested t tests and nested one-way ANOVA using data within these tables

More Sophisticated Statistical Analyses

Perform repeated measures ANOVA – even with missing data

Now Prism will automatically fit a mixed effects model to complete this analysis

Perform simple and multiple logistic regression

Fit a model to a binary outcome (yes/no, win/lose, pass/fail) based on one predictor variable (simple logistic regression) or many predictor variables (multiple logistic regression)

Principal Component Analysis

Calculate and select the principal components that describe the greatest amount of variance in your data. Choose from selection techniques including Parallel Analysis via Monte Carlo simulation, Eigenvalue threshold, Proportion of Variance threshold, and more

Multiple t test (and nonparametric) analyses

Perform multiple independent two-sample comparison tests simultaneously. Select from parametric/nonparametric tests, and specify if data are unpaired/paired

Analyze categorical variables with text in Multiple Linear and Multiple Logistic Regression

No coding required! Prism will automatically encode categorical variables and perform the analysis. Specify a reference and organize all levels of categorical variables in a model for clear, readable results

Interpolation from multiple linear and multiple logistic regression

Use the specified model to predict values for the dependent variable based on data in the data table or using theoretical values specified in the analysis

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Discover the Breadth of Statistical Features Available in Prism

Statistical Comparisons

Paired or unpaired t tests. Reports P values and confidence intervals.
Automatically generate volcano plot (difference vs. P value) from multiple t test analysis.
Nonparametric Mann-Whitney test, including confidence interval of difference of medians.
Kolmogorov-Smirnov test to compare two groups.
Wilcoxon test with confidence interval of median.
Perform many t tests at once, using False Discovery Rate (or Bonferroni multiple comparisons) to choose which comparisons are discoveries to study further.
Ordinary or repeated measures ANOVA followed by the Tukey, Newman-Keuls, Dunnett, Bonferroni or Holm-Sidak multiple comparison tests, the post-test for trend, or Fisher’s Least Significant tests.
One-way ANOVA without assuming populations with equal standard deviations using Brown-Forsythe and Welch ANOVA, followed by appropriate comparisons tests (Games-Howell, Tamhane T2, Dunnett T3)
Many multiple comparisons test are accompanied by confidence intervals and multiplicity adjusted P values.
Greenhouse-Geisser correction so repeated measures one-, two-, and three-way ANOVA do not have to assume sphericity. When this is chosen, multiple comparison tests also do not assume sphericity.
Kruskal-Wallis or Friedman nonparametric one-way ANOVA with Dunn's post test.
Fisher's exact test or the chi-square test. Calculate the relative risk and odds ratio with confidence intervals.
Two-way ANOVA, even with missing values with some post tests.
Two-way ANOVA, with repeated measures in one or both factors. Tukey, Newman-Keuls, Dunnett, Bonferroni, Holm-Sidak, or Fisher’s LSD multiple comparisons testing main and simple effects.
Three-way ANOVA (limited to two levels in two of the factors, and any number of levels in the third).
Analysis of repeated measures data (one-, two-, and three-way) using a mixed effects model (similar to repeated measures ANOVA, but capable of handling missing data).
Kaplan-Meier survival analysis. Compare curves with the log-rank test (including test for trend).
Comparison of data from nested data tables using nested t test or nested one-way ANOVA (using mixed effects model).

Nonlinear Regression

Fit one of our 105 built-in equations, or enter your own. Now including family of growth equations: exponential growth, exponential plateau, Gompertz, logistic, and beta (growth and then decay).
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 sum-of-squares 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.
Fit straight lines to two data sets and determine the intersection point and both slopes.

Principal Component Analysis (PCA)

Component selection via Parallel Analysis (Monte Carlo simulation), Kaiser criterion (Eigenvalue threshold), Proportion of Variance threshold, and more.
Automatically generated Scree Plots, Loading Plots, Biplots, and more.
Use results in downstream applications like Principal Component Regression.

Multiple Variable Graphing

Specify variables defining axis coordinates, color, and size.
Create Bubble Plots.

Column Statistics

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Calculate descriptive statistics: min, max, quartiles, mean, SD, SEM, CI, CV, skewness, kurtosis.
Mean or geometric mean with confidence intervals.
Frequency distributions (bin to histogram), including cumulative histograms.
Normality testing by four methods (new: Anderson-Darling).
Lognormality test and likelihood of sampling from normal (Gaussian) vs. lognormal distribution.
Create QQ Plot as part of normality testing.
One sample t test or Wilcoxon test to compare the column mean (or median) with a theoretical value.
Identify outliers using Grubbs or ROUT method.
Analyze a stack of P values, using Bonferroni multiple comparisons or the FDR approach to identify 'significant' findings or discoveries.

Simple 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 in four different ways (including QQ plot).
Compare slopes and intercepts of two or more regression lines.
Interpolate new points along the standard curve.
Pearson or Spearman (nonparametric) correlation.

Generalized Linear Models (GLMs)

Generate models relating multiple independent variables to a single dependent variable using the new multiple variables data table.
Multiple linear regression (when Y is continuous).
Poisson regression (when Y is counts; 0, 1, 2, …).
Logistic regression (when Y is binary; yes/no, pass/fail, etc.).

Clinical (Diagnostic) Lab Statistics

Bland-Altman plots.
Receiver operator characteristic (ROC) curves.
Deming regression (type ll linear regression).