If you are a student, teacher, or working with scientific data, BioStat Prime is a great statistical software to check your results using T-Test. Many people search “how to do a t-test in biostat prime” or “how to run t-test in biostat prime,” this guide will help answer all those questions in one place.
What is a T-Test?
A t-test is a fundamental statistical test used to compare the means of two groups to determine if they are significantly different from each other. It is widely used in clinical research, academic studies, and data science to validate hypotheses based on sample data.
For example:
- If you want to know if Group A and Group B have the same test scores
- Or if a medicine works better than another
How To Run a T-Test? (Step By Step Guide)
Follow these easy steps to run an independent t-test in BioStat Prime:
- Import your dataset in BioStat Prime.
- Click on the “Analysis” tab in the top menu.
- Navigate to “Mean”.
- From the drop-down, select “t test – Independent Samples”.
- In the dialog box:
- Select the dependent variable (e.g., Concentration).
- Select the factor variable with two levels (e.g., Treatments).
- Choose your alternative hypothesis:
- Two-sided (default): Checks for any difference.
- One-sided: Checks if one group is specifically higher or lower.
- Click the “Run” button to execute the analysis.
- Interpreting the Results
- Two-tailed p-value: Use this to determine if there is a significant difference between group means in either direction.
- One-tailed p-value: Generally ignored unless you had a specific directional hypothesis before starting the experiment.
- Interpreting the p-value:
- p < 0.05: Statistically significant difference.
- p ≥ 0.05: No significant difference.
You’ve now completed the BioStat Prime t test steps.
Types of t-Tests
- Independent Samples t-Test : An Independent t-test (also known as an unpaired t-test) is a statistical method which is used to compare the means of two independent groups. It helps determine whether the difference between the two groups is statistically significant or just due to random chances. (e.g., Treatment vs. Control).
- Paired Samples t-Test : A Paired t-test (also called a dependent t-test) is a statistical method used to compare means from the same group at two different times. It helps determine whether the difference between these paired observations is statistically significant. (e.g., before and after treatment).
- One-Sample t-Test : Compares the sample mean against a known or hypothesized population mean. One sample t test is used when you have one group of data and you want to compare the sample mean to a specific value and when the population standard deviation is unknown and the data is approximately normally distributed. This test is helpful when comparing your sample to a standard value or target.
| T-Test Type | What It Does |
|---|---|
| Independent t-test | Compares 2 different groups (like treatment vs control) |
| Paired t-test | Compares same group before and after (like test scores) |
| One-sample t-test | Compares 1 group to a known number (like a standard score) |
When Should You Use an Independent t-Test?
Use independent t-test if:
- You have two independent groups.
- The dependent variable is continuous (e.g., blood pressure, enzyme activity).
- Your data is approximately normally distributed.
- The groups have similar variances (homogeneity of variance).
Paired t-test table
The given below paired t test table is used to organize data for performing a paired t-test (or dependent t-test). This test compares two related groups (e.g., measurements before and after treatment) to determine whether there is a statistically significant difference between their means.
| Subject | Group 1 (Before) | Group 2 (After) | Difference (D = After - Before) | D² |
|---|---|---|---|---|
| 1 | ||||
| n | ||||
| Sum | ΣD | ΣD² |
Example of a paired t-test
Let’s take an example of a researcher trying to determine—they measure the blood pressure of 5 patients before and after taking the drug.
| Patient | Before (X₁) | After (X₂) | Difference (D = X₂ - X₁) | D² |
|---|---|---|---|---|
| 1 | 150 | 140 | -10 | 100 |
| 2 | 160 | 145 | -15 | 225 |
| 3 | 170 | 155 | -15 | 225 |
| 4 | 155 | 150 | -5 | 25 |
| 5 | 165 | 150 | -15 | 225 |
| Total | ΣD = -60 | ΣD² = 800 |
- Mean of the Differences (D̄) = -12
- Standard Deviation (SD) of Differences ≈ 4.47
- t-Statistic = -6.00
- Degrees of Freedom (df) = 5 - 1 = 4
- Critical t-value (two-tailed, α = 0.05, df = 4) ≈ ±2.776
- Since the calculated t = -6.00 is less than -2.776, we reject the null hypothesis.
- Conclusion: The drug has a statistically significant effect on reducing blood pressure.
One Sample T-Test
One-Sample t-Test is used when you want to compare the mean of a single sample to a known or hypothesized population mean.
Example of One sample t-test
Suppose nutritionists claim that the average sugar level in a new energy drink is 25 grams and a health researcher tests 7 samples and finds:
| Sample # | Sugar Content (g) |
|---|---|
| 1 | 28 |
| 2 | 26 |
| 3 | 24 |
| 4 | 30 |
| 5 | 22 |
| 6 | 27 |
| 7 | 25 |
- Sample Mean (x̄) = 26.00 g
- Standard Deviation (SD) = 2.65 g
- Sample Size (n) = 7
- Hypothesized Mean (μ) = 25 g
- t-statistic = 1.00
- p-value = 0.356
Since the p-value (0.356) is greater than 0.05, we fail to reject the null hypothesis.
Interpretation: There is no statistically significant difference between the mean sugar content and the hypothesized 25g.
T-Test Rules (Assumptions)
- Your data must be numbers (not words)
- Data should be normal (not too spread out)
- Groups should be independent (not related)
- For paired t-test, groups must be linked (e.g., same person)
T-Test in Biostatistics – FAQs
You interpret the t-test by looking at the p-value. If p < 0.05, the result is considered statistically significant.
A p-value tells you the probability of observing your results under the null hypothesis. A smaller p-value (< 0.05) suggests strong evidence against the null hypothesis.
BioStat Prime, a biostatistical software, can be used to perform t-tests efficiently.
A paired t-test compares means from the same group at different times, while an unpaired (independent) t-test compares means from two unrelated groups.
The key assumptions are: (1) The data are continuous (interval or ratio scale), (2) The data are approximately normally distributed, (3) Variances are equal for independent t-tests, and (4) Observations are independent (except for paired t-tests).
Yes. T-tests are designed for small sample sizes (n < 30) and are especially useful when the population standard deviation is unknown.
Example: t(18) = 2.45, p = 0.024. Always include the degrees of freedom, t-statistic, and p-value.
A t-test compares two groups, whereas ANOVA compares three or more groups simultaneously.
What is the critical value in a t-test?
The critical value is the cutoff point from the t-distribution table. If your calculated t-value exceeds it, the result is statistically significant.
Final thoughts
Now that you’ve learned how to perform a t-test in BioStat Prime, you can see just how quick and intuitive the process is. Whether you're validating research findings or comparing group outcomes, BioStat Prime makes Biostatistical testing straightforward even for beginners.
By preparing your data and following the step-by-step approach outlined above, you can run any type of t-test—one-sample, paired, or independent with just a few clicks. It’s a powerful way to determine whether your results are truly significant, without the complexity.