Inference for Quantitative Data—Means

These flashcards cover key vocabulary and concepts related to inference for quantitative data and means, including t-distribution, one-sample and two-sample t-intervals, hypotheses, and significance testing.

t-distribution

A distribution that varies from the normal model, with more area allocated to the tails and a lower peak, influenced by degrees of freedom.

degrees of freedom (df)

Calculated as n - 1 for estimating a population mean.

one-sample t-interval

A confidence interval procedure for estimating the mean of a single quantitative variable when the population standard deviation is unknown.

conditions for one-sample t-interval

Requirements including random sampling, 10% condition, and normality or large enough sample size.

two-sample t-interval

A method to create a confidence interval for the difference between two population means using two independent sample means.

hypotheses for one-sample t-test

Null hypothesis (H0): μ = μ0, Alternative hypothesis (HA): μ > μ0, μ < μ0, or μ ≠ μ0.

P-value

The probability of obtaining evidence for the alternative hypothesis as strong or stronger than the observed evidence if the null hypothesis is true.

significance test conclusion

If the P-value is small, the null hypothesis (H0) is rejected; if it is not small, we fail to reject H0.

paired t-interval

A confidence interval procedure for estimating the mean difference from paired data.

conditions for two-sample t-test

Random sampling, 10% condition, and normality or large enough sample size for both groups.

matched pairs

A type of paired data from two values of the same quantitative variable for each individual or similar individuals.