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1) The t distribution depends on degrees of freedom and is heavier-tailed than the normal for small df.
2) As \( n \to \infty \), the t distribution approaches the standard normal distribution.
3) To use a one-sample t-interval, the population standard deviation \( \sigma \) must be known.
4) For the same confidence, t-critical values are larger than z-critical values when \( n \) is small.
5) The degrees of freedom for a one-sample t interval are \( n \).
6) The standard error in the one-sample t statistic is \( s/\sqrt{n} \).
7) The t distribution has lighter tails than the standard normal for all df.
8) The value of df is irrelevant when looking up t critical values.
9) For identical \( \bar x, s, n \), a t-interval is typically narrower than a z-interval.
10) You can compute exact t p-values without knowing the degrees of freedom.