The Friedman Test is the non-parametric alternative to the one-way ANOVA with repeated measures. It is used to test for differences between groups when the dependent variable is ordinal.
To perform a Friedman Test for a given dataset, simply enter the values for up to five samples into the cells below, then press the “Calculate” button.
The calculator will output the test statistic Q, the p-value of the test, and the calculations that were used to derive the test statistic Q.
| Group 1 | Group 2 | Group 3 | Group 4 | Group 5 |
|---|---|---|---|---|
Test Statistic Q:
p-value:
Solution
Q =
Q =
Q =
Hi,
Can I accurately perform a Friedman test with a sample size of 4?
The Q formula, I think, is not considering the possible presence of ties
Hi Thang…Thank you for your feedback! Let us know if we can help answer any questions.
Hi, Zach! Can we use Friedman Test using different properties of a product? I have 3 values of 6 properties.
Hi Ghia…Yes, you can use the **Friedman test** to compare different properties of a product, especially when you have repeated measurements or multiple related groups (as in your case, different properties with 3 values each).
### How the Friedman Test Works:
The Friedman test is a **non-parametric statistical test** used to detect differences between groups when the same subjects (or items) are measured multiple times. It’s often used when the assumptions of a repeated-measures ANOVA are violated.
### In Your Case:
You mentioned you have 3 values for 6 properties, which can be viewed as 6 groups with 3 measurements each. The Friedman test can determine whether there are statistically significant differences between the ranks of these properties.
### Example:
Let’s say you have 6 properties (A, B, C, D, E, F) and for each property, you have 3 values:
| | Property A | Property B | Property C | Property D | Property E | Property F |
|—–|————|————|————|————|————|————|
| 1st | 10 | 12 | 8 | 11 | 9 | 7 |
| 2nd | 11 | 14 | 9 | 12 | 10 | 8 |
| 3rd | 12 | 13 | 10 | 13 | 11 | 9 |
### Applying the Friedman Test:
1. **Null Hypothesis**: All properties perform equally well (no significant difference between them).
2. **Alternative Hypothesis**: At least one property performs differently from the others.
In Python, you can run the Friedman test using `scipy.stats.friedmanchisquare()`:
“`python
import scipy.stats as stats
# Example data for 6 properties with 3 measurements each
property_A = [10, 11, 12]
property_B = [12, 14, 13]
property_C = [8, 9, 10]
property_D = [11, 12, 13]
property_E = [9, 10, 11]
property_F = [7, 8, 9]
# Running the Friedman test
stat, p = stats.friedmanchisquare(property_A, property_B, property_C, property_D, property_E, property_F)
print(f”Friedman test statistic: {stat}”)
print(f”P-value: {p}”)
# Interpret the result
if p < 0.05: print("There is a significant difference between properties.") else: print("No significant difference between properties.") ``` ### Interpreting Results: - **P-value**: If the p-value is less than your significance level (commonly 0.05), you reject the null hypothesis, meaning there are significant differences between the properties. - **If the test is significant**: You might want to perform post-hoc tests (e.g., Dunn's test) to determine which specific properties differ. Let me know if you need further clarification or help with your data!