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Does z-curve offer options for small sample (small-N) literatures like animal research?
Short answer:
Yes — z-curve 3.0 adds new transformation methods and a t-curve option that make the method more appropriate for analyses involving small samples (e.g., N < 30). These options are designed to mitigate biases that arise when you convert small-sample test statistics to z-scores using standard normal approximations. Z-curve.3.0 also allows researchers to use t-distributions (t-curve) with a fixed df that is more similar to the distributions of test statistics from small samples than the standard normal distribution.
Details:
- The z-curve 3.0 tutorial (Chapter 8) explains that instead of only converting p-values to z-scores, you can now:
- Try alternative transformations of t-values that better reflect their sampling distribution, and
- Use a direct t-curve model that fits t-distributions with specified degrees of freedom instead of forcing a normal approximation. This “t-curve” option is recommended when studies have similar and genuinely small degrees of freedom (like many animal experiments). (replicationindex.com)
- These improvements help reduce bias introduced by naïve normal transformations, though they don’t completely eliminate all small-sample challenges, and performance can still be unstable when degrees of freedom vary widely or are extremely small. (replicationindex.com)
Link to the tutorial:
🔗 Z-Curve 3.0 Tutorial (Introduction and links to all chapters) — https://replicationindex.com/2025/07/08/z-curve-tutorial-introduction/ (replicationindex.com)