Statistical Concepts and Confidence Intervals

These flashcards cover key concepts related to statistical testing, confidence intervals, hypothesis testing, and their interpretations.

What is a t-test used for in statistics?

A t-test is used to determine if there is a significant difference between the means of two groups.

What does a p-value indicate in hypothesis testing?

A p-value indicates the probability of obtaining test results at least as extreme as the observed results, under the assumption that the null hypothesis is true.

What are the components needed to perform a t-test in RStudio?

You need a sample of quantitative data, the hypothesized mean under the null (m), an alternative hypothesis (greater, less, two.sided), and the confidence level.

What is the effect size and why is it important?

Effect size quantifies the strength of a phenomenon and is important for interpreting the practical significance of statistical results.

What is the purpose of a confidence interval?

A confidence interval provides a range of plausible values for a population parameter and indicates the reliability of the estimation process.

What happens to a confidence interval when the confidence level increases?

When the confidence level increases, the confidence interval becomes wider.

How do you calculate the one-proportion confidence interval?

The one-proportion confidence interval is calculated using the formula: p̂ ± z*(√[p̂(1 - p̂) / n]), where p̂ is the sample proportion and n is the sample size.

What is a common mistake when interpreting confidence intervals?

A common mistake is to say there is a probability that the true parameter lies within the interval instead of saying that the interval was constructed in such a way that it will include the true parameter a certain percentage of the time.

How is a symmetric confidence interval different from an asymmetric one?

A symmetric confidence interval has the same width on both sides of the estimate, while an asymmetric one has different widths, often resulting from one-tailed tests.

What is meant by 'effect sizes are useful for comparing'?

Effect sizes allow for the comparison of the magnitude of results across different studies, regardless of sample size or statistical significance.