**Table of Contents:**

**t-tests**

In the navigation panel to the left of the spreadsheet, click "Add Analysis".

If your dataset has two groups, a t-test will be suggested. If your dataset has three or more groups, a one-way ANOVA will be suggested.

We calculate descriptive statistics (mean, median, standard deviation, etc.) automatically with every test.

BioRender offers a few different t-tests. 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 Shapiro-Wilk 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 t-test

Paired t-test

Welch’s t-test

Mann-Whitney U Test

Wilcoxon matched pairs signed ranks test

**One-way ANOVAs**

In the navigation panel to the left of the spreadsheet, click "Add Analysis".

If your dataset has two groups, a t-test will be suggested. If your dataset has three or more groups, a one-way 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 one-way 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 Shapiro-Wilk 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:

One-way ANOVA

Welch’s ANOVA

Kruskal-Wallis test

**Two-way ANOVAs**

In the navigation panel to the left of the spreadsheet, click "Add Analysis".

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 comparisons 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

Games-Howell

**Linear Regressions**

In the navigation panel to the left of the spreadsheet, click "Add Analysis".

Select your regression model

Select whether you want to set the Y-intercept to zero

Click Run

**Dose-response regression**

*Alternate names include dose-response 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"

Have any questions or concerns?

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