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Edited by: Published: 2017
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A t-test is a statistical test of the differences between sample populations, assessing how data about the sample population differs from what is observed in the actual population. Similar to a z-test, the findings of a t-test tell researchers at what value(s) on the normal curve the null hypothesis can be rejected, indicating a change in the sample population greater than what can be expected by chance. However, there is always a difference between what researchers observe, compared to what occurs in the actual population, generating a standard error. The ability for error increases when there is a small sample size (N). For example, if researchers examined what studying techniques are most likely to reduce test-taking anxiety among college students, but only sampled ...

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