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T-tests assess differences between two groups when population variance is unknown or sample sizes are small (n < 30).
Z-tests are suitable for large sample sizes (n > 30) with a known population variance, a rare but powerful scenario.
The choice between t-tests and z-tests hinges on sample size: t-tests for fewer than 30 samples, z-tests for more.
The selection of t-test or z-test depends on whether the population variance is known, impacting the test's applicability.
T-tests use the Student's t-distribution, accommodating more variability, especially with smaller or uncertain data sets.
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Z-tests rely on the standard normal distribution, ideal for clear, large sample analyses with known variance.
Both tests assume data independence and normality. These prerequisites are vital for accurate, reliable outcomes.
Statistical tests require that each data point is independent, ensuring no single observation influences another.
For both t-tests and z-tests, data or residuals should follow a normal distribution, a cornerstone for precise analysis.
The t-distribution's thicker tails provide a more adaptable approach in t-tests, beneficial for analyzing diverse data types.
Delve deeper into statistical tests by accessing the complete article on t-tests vs z-tests for thorough insights.