Performing a statistical analysis

BioRender provides a comprehensive suite of statistical analysis tools to assist in interpreting your experimental data. This guide outlines the available analyses and provides step-by-step instructions for conducting each type.

Table of contents:

• t-tests
• One-way ANOVAs
• Two-way ANOVAs
• Multiple comparisons tests
• Linear regressions
• Dose-response regression

t-tests

To compare differences between two groups, BioRender offers several t-test options:

• Unpaired t-test
• Paired t-test
• Welch’s t-test
• Mann-Whitney U Test
• Wilcoxon matched pairs signed ranks test

Setting up a t-test

1. In the navigation panel to the left of the spreadsheet, click the plus sign (+) next to the Analysis subsection of your dataset.

2. If your dataset has two groups, a t-test will be suggested. Descriptive statistics (mean, median, standard deviation, etc.) are automatically calculated with every test.

3. Use the settings in the New Analysis popup to select the appropriate test for your experiment.

• • BioRender automatically runs two preliminary tests:
    • Shapiro-Wilk normality test: Checks if your dataset is normally distributed.
    • Levene’s test for equality of variances: Assesses if variances are equal across groups.
  • Based on the results, BioRender suggests the most suitable test, indicated by a suggestion icon. Hover over the icon for an explanation.

• • The results of both tests are included in your analysis output.

Note: These suggestions are guidelines. It's essential to consider the conventions of your field and understand the different options to make an informed decision.

One-way ANOVAs

For comparing differences between three or more groups, BioRender offers:

• One-way ANOVA
• Welch’s ANOVA
• Kruskal-Wallis test

Setting up a One-way ANOVA

1. In the navigation panel to the left of the spreadsheet, click the plus sign (+) next to the Analysis subsection of your dataset.

2. If your dataset has three or more groups, a one-way ANOVA will be suggested. Descriptive statistics are automatically calculated with every test.

3. Use the settings in the New Analysis popup to select the appropriate test for your experiment.

• • BioRender runs the Shapiro-Wilk and Levene’s tests to suggest the best option, indicated with a suggestion icon. Hover over the icon for an explanation.

• • The results of both tests are included in your analysis output.

Interpreting One-way ANOVA results

• If the ANOVA result is significant, you may need to perform multiple comparisons tests to determine which specific groups differ.
• Choose the appropriate post-hoc test based on your data distribution and variance equality.

Note: These suggestions are guidelines. Consider the conventions of your field and understand the different options to make the most informed decision.

Two-way ANOVAs

To analyze the interaction between two independent variables, follow these steps:

1. In the navigation panel to the left of the spreadsheet, click the plus sign (+) next to the Analysis subsection of your dataset.

2. Select the groups you want to compare (minimum of 2 per independent variable).

1. Choose your experimental design.
2. Decide whether to run multiple comparisons tests:
   • If yes, choose which groups to compare.
   • Select your multiple comparisons test. BioRender suggests an appropriate test based on your comparison groups, indicated with a suggestion icon. Hover over the icon for an explanation.

3. Click "Run" to execute the analysis.

Multiple Comparisons tests

After conducting an ANOVA, if a significant difference is found, multiple comparisons tests help identify which specific groups differ.

Selecting a Multiple Comparisons test

1. In the New Analysis popup, opt to run a multiple comparisons test.
2. Choose whether to compare all groups against each other or just against a control group.
3. Based on your selection, BioRender suggests an appropriate test.

Available tests

Here are all of the multiple comparison tests offered:

• Tukey 
• Dunnett
• Bonferroni
• Dunnett T3
• Games-Howell

Linear Regressions

To analyze the relationship between two continuous variables:

1. In the navigation panel to the left of the spreadsheet, click the plus sign (+) next to the Analysis subsection of your dataset.

2. Select your regression model.

3. Decide whether to set the Y-intercept to zero.

4. Click Run to perform the regression analysis.

Dose-Response Regression

Also known as dose-response curves, Hill curves, Hill equation, or EC50/IC50 analysis, this regression analyzes the effect of varying doses on a response.

Setting up a Dose-Response Regression

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

2. Select the type of experiment: either stimulation or inhibition, then click Continue.

3. If you've already logarithmically transformed your X values using base 10, select Yes; otherwise, select No.

4. Decide if you want to constrain your Hill slope to 1, depending on your experimental setup.

5. Click Run to execute the dose-response regression.

Summary

• t-tests – Comparing two groups
• One-way ANOVA – Comparing three or more groups
• Two-way ANOVA – Analyzing interaction between two independent variables
• Multiple comparisons – Identifying which groups differ after an ANOVA
• Linear regression – Assessing the relationship between two continuous variables
• Dose-response regression – Evaluating dose-response relationships

Related articles

• For more information, visit our Graph collection.

Need help?

1. Email: support@biorender.com
2. Live Chat: Available by clicking on the "Help" bubble in the app on the bottom right-hand corner.