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The crossover point between the T and Z tests is commonly recognized as a sample size of 30. This is based on the Central Limit Theorem, which states that as the sample size increases, the distribution of the sample mean approaches a normal distribution, regardless of the shape of the population distribution.

When the sample size is 30 or larger, the sampling distribution of the mean can typically be approximated using the normal distribution, which is the basis for using the Z test. For smaller sample sizes, particularly less than 30, the T distribution is often more appropriate because it accounts for the additional variability present in smaller samples.

In the context of hypothesis testing, the Z test assumes a larger sample size and a known population variance, while the T test is used for smaller sample sizes where the population variance is unknown. This distinction is essential for selecting the appropriate test based on sample size and the characteristics of the data.

Thus, the sample size of 30 is a practical guideline that helps in choosing between the two tests, underscoring the importance of sample size in statistical inference.