This interactive app is designed to help biologists learn and apply basic statistical concepts essential for analyzing biological data, conducting experiments, and drawing valid conclusions. Statistics is a critical tool for biologists in fields such as genetics, ecology, and epidemiology, enabling them to make evidence-based decisions and communicate findings effectively. Our app provides an easy-to-use platform for mastering key statistical techniques commonly used in biological research.
Biologists frequently work with large datasets, and statistical analysis helps make sense of complex biological information. The app helps users:
- Analyze biological data (e.g., gene expression, species population)
- Design experiments with scientifically rigorous methods
- Make inferences and test hypotheses
- Understand relationships between variables
- Visualize and interpret results
The app provides a hands-on introduction to five key statistical methods frequently used in biological research:
T-tests compare the means of two groups to assess whether any observed differences are statistically significant.
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Two-Sample T-Test: Compares means of two independent groups.
- Example: Comparing plant height between two different fertilizer treatments.
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Paired T-Test: Compares measurements from the same group under two conditions.
- Example: Measuring before-and-after weight changes in fish exposed to a drug.
The z-test compares a sample mean to a population mean, typically used when the sample size is large.
- Example: Testing if the average height of a species of tree has changed compared to historical data.
Understanding data distributions is key to selecting the right statistical tests.
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Normal Distribution: Often used to model biological data such as body sizes or enzyme activity.
- Example: Determining probabilities of measuring a certain enzyme level in a sample.
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Binomial & Poisson Distributions: Used for categorical or count-based data.
- Example: Modeling the probability of an event occurring (e.g., a species' presence in different habitats).
ANOVA tests whether there are significant differences between the means of three or more groups.
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One-Way ANOVA: Compares the means across multiple groups for one factor.
- Example: Comparing plant growth under different light conditions.
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Two-Way ANOVA: Evaluates two independent variables and their interaction.
- Example: Examining how both water and fertilizer affect plant growth.
Regression helps to model relationships between variables.
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Linear Regression: Models the relationship between one independent and one dependent variable.
- Example: Modeling the effect of temperature on algae growth.
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Multiple Regression: Assesses multiple predictors simultaneously.
- Example: Analyzing how light, temperature, and soil type together affect plant growth.
The app incorporates several engaging, interactive features to enhance the learning experience:
- Data Visualizations: Interactive graphs (e.g., histograms, scatter plots, box plots) to help users visualize distributions, regression lines, and test results.
- Code Examples and Walkthroughs: Step-by-step guides and sample code to show users how to apply statistical tests in practice.
- Quiz: A built-in quiz feature to test knowledge, with immediate feedback to reinforce concepts and improve understanding.
- Ecology: A biologist compares species diversity in two habitats using a t-test to determine if the difference is significant.
- Genetics: A researcher tests if a new drug affects the growth of bacteria using a z-test to compare growth rates.
- Clinical Trials: An ANOVA is used to test the effects of three drug treatments on patient outcomes.
- Environmental Science: A study uses linear regression to predict plant growth based on temperature data.
This app provides biologists with the statistical tools they need to analyze data, design experiments, and draw meaningful conclusions. By focusing on essential techniques like t-tests, z-tests, ANOVA, and regression analysis, the app helps users build a strong foundation in statistics, making complex data more accessible and interpretable.
Clone the repository in your command line by:
git clone https://github.com/hbrackney/StatsApp.git
Step into the repository by: (for Mac or Windows users)
cd StatsApp
All specific dependencies are included in the environment.yaml file. Create environment with required dependencies using the environment.yaml file:
conda env create -f environment.yaml
And then activate the environment:
conda activate StatsApp
From command line or in visual studio terminal, run the server.py file:
python app.py
Go to your preferred browser and enter
localhost:1000
The website is now live!
To end the website run and to regain typing ability in command line or terminal press Crtl + C.
05-12-2024 (LS): Updated README to expand explanations and images. Added tests to dash_apps.
05-12-2024 (SS): Added comments to app.py, plots.py, test_app.py, and test_plots.py.
04-12-2024 (SS): Created regressions tests to test_app.py, test_dash_apps.py, and test_plots.py
02-12-2024 (HB): Fixed Page mapping and added more comments to dash_apps.py and tests
01-12-2024 (SS): Completed Regressions page. Note: I added scikit-learn to the dependencies, so install that before running again.
19-11-2024 (LS): Added description to the About page.
19-11-2024 (LS): Updated t-test page and added python/R examples
19-11-2024 (LS): Added ANOVA page with tests for the dash apps and plots, and added python/R examples
19-11-2024 (HB): Changed ReadME.md installation instructions to be more through.
19-11-2024 (HB): Fixed Quiz on Distributions Page. Added more tests for plots.py and app.py.
18-11-2024 (LS): Updated and expanded README and CONTRIBUTING to include background info and description.
18-11-2024 (HB): Filled in Reference page to explain more and be more of a template.
18-11-2024 (HB): Added a figure on the home page that flips between images from some of the pages.
18-11-2024 (HB): Added python and R examples to Distributions Page. Added code/precode styles to the styles.css file.
12-11-2024 (SS): Added quiz to z-test page and added more thorough docstrings.
11-11-2024 (HB): Added Distribution Page with tests for the dash apps and plots.
06-11-24 (SS): Added example python script to z-test page and updated about page.
This version is an initial build to test basic functions and usablity.
30-10-2024 (LS): Added and fixed tests in the test_dash_apps.py through dash.testing.
30-10-2024 (HB): Removed t_quiz.py/quizzes.py (and corresponding test files). Corrected quiz code in t-test.html. Added tests for dash_apps.py and plots.py
30-10-2024 (SS): Updated data tabes and plots for z-test page so that an updated box plot, z-statistic value, and p-value are correctly displayed when the user inputs new data points into the table.
25-10-2024 (HB): Spilt app.py into app.py, dash_apps.py, plots.py to make things easier to read.
25-10-2024 (SS): Added z-test example info to the z-test page
23-10-2024 (HB): Added a knowledge quiz to the t-test page
23-10-2024 (HB): Added tests for app initialization
23-10-2024 (HB): Added a navigation bar
23-10-2024 (SS): Fixed formatting errors on z-test page
21-10-2024 (SS): Added z-test text information
21-10-2024 (LS): Added t-test text information with images and updated general formatting
16-10-2024 (HB): Added interactive plots to the content pages (t test, z test, example page)
07-10-2024 (HB): Changed type entry box to dropdown menu for users
07-10-2024 (HB): Added "Return Home" button to each page
07-10-2024 (HB): Created template html files to create inital GUI
07-10-2024 (HB): Created server.py file to create web app