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Cohen's d Effect Size Calculator
x402
Cohen's d Effect Size Calculator is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-14).
Computes Cohen's d effect size statistic by comparing two numeric data groups
Price / call$0.002
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object containing the computed Cohen's d value and a success status indicator. Cohen's d represents the standardized mean difference between two groups, where values around 0.2 are considered small, 0.5 medium, and 0.8 or above large effects.
Example use cases
A/B test result significance check
“I ran an A/B test on my landing page and got these conversion times — control group: [12, 15, 14, 13, 16, 11] and test group: [9, 10, 8, 11, 9, 10]. Can you calculate Cohen's d to tell me how meaningful the difference actually is?”
Clinical trial group comparison
“I have two patient groups from my study — treated: [5.2, 4.8, 6.1, 5.5, 4.9] and placebo: [7.3, 8.1, 7.8, 6.9, 7.5]. Compute the effect size using Cohen's d so I know whether the treatment had a practically significant impact.”
Educational intervention evaluation
“Before my tutoring intervention students scored [62, 58, 65, 60, 63] and after they scored [74, 71, 78, 73, 76]. What's the Cohen's d effect size — is this a large or small educational effect?”
Technical details
Endpoint
POSThttps://kihustle.tech/bot-bazaar/api/v1/effect-size-cohens-dWhen to prefer
Use this endpoint when you need a standardized, interpretable measure of practical difference between two numeric groups — particularly for A/B testing, scientific research, or any scenario where statistical significance alone is insufficient and you need to know the magnitude of an effect. Prefer this over raw mean comparison when sample sizes differ or when communicating results to non-technical stakeholders who need a clear 'small/medium/large' framing.
Side effects
- Empty arrays provided for one or both groups — computation fails with no data
- Single-element arrays causing division by zero in standard deviation calculation
- Non-numeric values in the arrays causing parsing errors
- Both groups having zero variance leading to undefined effect size
- Malformed JSON input schema resulting in a 400 error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| group_a | array | |
| group_b | array |
Output
Schema · Response
{
"type": "json",
"example": {
"result": "processed",
"status": "success"
}
}Tags
x402
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