GraphPad Prism

The best analytical and graphing solution for scientific study. Join the world's finest scientists and learn how to utilize GraphPad Prism to save time, make better analysis decisions, and beautifully graph and display your scientific findings. 

Unlike spreadsheets or other scientific graphing tools, it has eight different types of data tables that are precisely prepared for the studies you wish to perform. This makes it easy to enter data accurately, select appropriate analyses, and create visually appealing graphs. 

Avoid using statistical jargon. It clearly shows a comprehensive library of studies ranging from common to highly specific—nonlinear regression, t-tests, nonparametric comparisons, one-, two-, and three-way ANOVA, contingency table analysis, survival analysis, and much more. Each analysis includes a checklist to help you understand the relevant statistical assumptions and ensure you've chosen the right test.

Reduce the complexity of statistics. Prism's online help exceeds your expectations. At practically every step, you can access thousands of pages from the online Prism Guides. Browse the Graph Portfolio and learn how to create a variety of graph types. Tutorial data sets can also assist you learn why you should run a particular analysis and how to interpret the findings. 

No other program simplifies curve fitting as much as Prism. Select an equation, and GraphPad Prism will fit the curve, provide a table of results and function parameters, draw the curve on the graph, and interpolate unknown values. 

No coding is required. Graphs and results are dynamically updated in real-time. Any modifications to the data and analyses, such as adding missing data, removing incorrect data, correcting errors, or altering analysis options, are immediately reflected in the findings, graphs, and layouts. 

Reduce the number of tiresome procedures required to analyze and graph a set of trials. You may easily repeat your work by building a template, copying a family, or cloning a graph, saving you hours of setup time. With a single click, use the Magic to give a set of graphs a consistent appearance. 

Focus on the story in your data rather than altering your program. It makes it simple to design the graphs you want. Select the type of graph and personalize any aspect, including how the data is organized, the style of your data points, labels, fonts, colors, and much more. The customizing possibilities are limitless. 

Reduce the time required to publish. It helps you to tailor your exports to journal specifications (file type, resolution, transparency, size, color space RGB/CMYK). Set defaults to save time. 

Share more than just graphs. Prism's thorough record of your data allows for productive collaboration with other scientists. All of your project's components (raw data, analyses, findings, graphs, and layouts) are included in a single file that you can easily distribute. Others can now easily track your progress at each stage, improving the clarity of your results and expediting your joint efforts.

Features and Highlights

• Paired and unpaired t-tests. Reports P-values and confidence intervals. 
• Nonparametric Mann-Whitney test with a confidence interval for the difference between medians. 
• Use the Kolmogorov-Smirnov test to compare two groups. 
• Wilcoxon test using a median-based confidence interval. 
• Perform numerous t-tests at once, utilizing the False Discovery Rate (or Bonferroni multiple comparisons) to determine which comparisons are findings worth investigating further. 
• Ordinary or repeated measurements one-way ANOVA, followed by the Tukey, Newman-Keuls, Dunnett, Bonferroni, or Holm-Sidak multiple comparison tests, the post-trend test, or Fisher's Least Significant tests. 
• Many multiple comparison tests include confidence intervals and multiplicity-adjusted P values. 
• Greenhouse-Geisser adjustment allows repeated measures one-way ANOVA to be performed without assuming sphericity. When this option is selected, multiple comparison tests do not presume sphericity. 
• Kruskal-Wallis or Friedman nonparametric one-way ANOVA with Dunnett's post-test. 
• Fisher's exact test or chi-squared test. Calculate the relative risk and odds ratios using confidence intervals. 
• With some post-tests, two-way ANOVA is used, even when there are missing values. 
• Two-way ANOVA involves repeated measurements for one or both components. Tukey, Newman-Keuls, Dunnett, Bonferron, Holm-Sidak, or Fisher's LSD multiple comparisons are used to test main and simple effects. 
• Three-way ANOVA (with two levels in two components and any number of levels in the third). 
• Kaplan-Meier survival analysis. Compare curves using the log-rank test (which includes a trend test). 
• Calculate min, max, quartiles, mean, SD, SEM, CI, CV, and geometric means using confidence intervals. 
• Frequency distributions (bin to histogram), which include cumulative histograms. 
• Three methods were used to test for normality. 
• To compare the column mean (or median) to a theoretical number, use a one-sample t-test or the Wilcoxon test. 
• Skewness and Kurtosis. 
• Identify outliers using the Grubbs or ROUT approach. 
• Calculate the slope and intercept using confidence intervals. 
• Force the regression line to pass through a particular location. 
• Fit to replicate Y values or get the mean Y. 
• A runs test can be used to detect a divergence from linearity. 
• Calculate and graph the residuals. 
• Compare the slopes and intercepts of two or more regression lines. 
• Interpolate new points on the standard curve. 
• Pearson and Spearman (nonparametric) correlations. 
• To identify "significant" results or discoveries, analyze a stack of P values using Bonferroni multiple comparisons or the FDR method. 
• Bland-Altman plots. 
• Receiver operator characteristic (ROC) curves. 
• Deming regression (Type ll linear regression). 
• Simulate XY, column, and contingency tables. 
• Perform Monte-Carlo studies on simulated data. 
• Plot functions based on equations you select or enter and parameter values you specify. 
• The area under the curve, including the confidence interval. 
• Transform the data. 
• Normalize. 
• Identify outliers. 
• Normality testing. 
• Transpose the tables. 
• Subtract the baseline (then merge columns). 
• Calculate each value as a fraction of the row, column, or grand total.

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