Table of Contents:
 ttests
 Oneway ANOVAs
 Twoway ANOVAs
 Multiple comparisons tests
 Linear regressions
 Doseresponse regression
ttests
 In the navigation panel to the left of the spreadsheet, click the plus sign (+) next to the Analysis subsection of your dataset.
 If your dataset has two groups, a ttest will be suggested. If your dataset has three or more groups, a oneway ANOVA will be suggested.
 We calculate descriptive statistics (mean, median, standard deviation, etc.) automatically with every test.
 BioRender offers a few different ttests. Use the settings in the New Analysis popup to select the appropriate test for your experiment.
 We automatically run two tests to help you pick the best option for your data: the ShapiroWilk normality test and the Levene’s test for equality of variances.
 Based on the results of the tests, we will suggest options to select in the New Analysis popup. These suggestions are highlighted with the icon shown below:
Hover over the icon for an explanation of why that option was suggested.
 The results of both tests are included in your analysis output.
 Note: these suggestions are just that – suggestions! Sometimes it might be better to follow the conventions of your field. When in doubt, it’s best to understand what these different options mean so you can make the most informed decision.
Here are all of the tests offered for comparing differences between two groups:
 Unpaired ttest
 Paired ttest
 Welch’s ttest
 MannWhitney U Test
 Wilcoxon matched pairs signed ranks test
Oneway ANOVAs
 In the navigation panel to the left of the spreadsheet, click the plus sign (+) next to the Analysis subsection of your dataset.
 If your dataset has two groups, a ttest will be suggested. If your dataset has three or more groups, a oneway ANOVA will be suggested.
 We calculate descriptive statistics (mean, median, standard deviation, etc.) automatically with every test.
 BioRender offers a few different variations of the oneway ANOVA. Use the settings in the New Analysis popup to select the appropriate test for your experiment.
 We automatically run two tests to help you pick the best option for your data: the ShapiroWilk normality test and the Levene’s test for equality of variances.
 Based on the results of the tests, we will suggest options to select in the New Analysis popup. These suggestions are highlighted with the icon shown below:
Hover over the icon for an explanation of why that option was suggested.  The results of both tests are included in your analysis output.
Note: these suggestions are just that – suggestions! Sometimes it might be better to follow the conventions of your field. When in doubt, it’s best to understand what these different options mean so you can make the most informed decision.
Here are all of the tests offered for comparing differences between three or more groups:


 Oneway ANOVA
 Welch’s ANOVA
 KruskalWallis test

Twoway ANOVAs
 In the navigation panel to the left of the spreadsheet, click the plus sign (+) next to the Analysis subsection of your dataset.
 Select which groups you want to compare (minimum 2 per independent variable)
 Select your experimental design
 Choose whether you want to run multiple comparisons tests or not
 If yes, choose which groups you want to compare
 Then choose your multiple comparisons test. One will be suggested to you based on your chosen comparison groups. These suggestions are highlighted with the icon shown below  hover over the icon for an explanation of why that option was suggested.
 Click Run
Multiple comparisons tests
ANOVA tests by themselves will tell you if there is a significant difference between at least two of the groups but you will need to do a multiple comparisons test to know which specific groups are significantly different from each other.
To run multiple comparison tests:
 Every ANOVA test will offer the option to run a multiple comparison test in the New Analysis popup.
 You first have the option to select if you want to compare all groups against each other or just against the control group.
 Based on your selection in Step 2, you’ll be given the option to run a specific multiple comparison test.
Here are all of the multiple comparison tests offered:
 Tukey
 Dunnett
 Bonferroni
 Dunnett T3
 GamesHowell
Linear Regressions
 In the navigation panel to the left of the spreadsheet, click the plus sign (+) next to the Analysis subsection of your dataset.
 Select your regression model
 Select whether you want to set the Yintercept to zero
 Click Run
Doseresponse regression
Alternate names include doseresponse curves, Hill curves, Hill equation, EC50/IC50 analysis.
 In the navigation panel to the left of the spreadsheet, click "Add Analysis".
 Select the type of experiment you ran: either "stimulation" or "inhibition", then click "Continue"
 If you have already logarithmically transformed your X values using base 10, select "Yes". Otherwise, select "No". You can also decide if you want to constrain your Hill slope to 1 or not depending on your experimental setup.
 Click "Run"