Inferential Statistics Flashcards
How do you perform hypothesis testing?
It involves setting up a null and alternative hypothesis, choosing a significance level, calculating the test statistic, and making a decision based on the comparison of the statistic with a critical value.
What are confidence intervals and what do they represent?
They estimate the range within which a population parameter lies with a certain level of confidence.
What is a Type I error in hypothesis testing?
The error of rejecting a true null hypothesis.
What is a Type II error in hypothesis testing?
The error of failing to reject a false null hypothesis.
Define power analysis in the context of statistical testing.
It refers to the probability that a statistical test will detect an effect when there is one.
What assumptions do you need to check before performing a t-test?
Normality of data, equal variance, and independence of observations.
How do you determine which type of t-test to use?
Based on the number of samples and whether the samples are independent or paired.
What is the difference between a one-tailed and a two-tailed test?
A one-tailed test looks for an effect in one direction, a two-tailed test in both directions.
What factors influence the width of a confidence interval?
Sample size, variability in the data, and confidence level.
How do sample size and variability affect statistical power?
Larger sample sizes and lower variability increase power.
What is the null hypothesis in a statistical test?
The hypothesis that there is no effect or no difference.
What is an alternative hypothesis?
It proposes that there is an effect or a difference.
Why is the significance level often set at 0.05?
It controls the acceptable rate of Type I error.
What is the p-value and how do you interpret it?
It measures the probability of observing the test results under the null hypothesis.
How does one reduce the likelihood of committing a Type I error?
By setting a more stringent significance level.
What are the consequences of a Type II error?
It might result in missing a genuine effect or difference.
How do you calculate the power of a test?
By the inverse relationship between beta (risk of Type II error) and power.
What role does effect size play in hypothesis testing?
It quantifies the size of the effect and is crucial for determining the test’s sensitivity.
How can increasing the sample size affect the outcomes of a test?
Increases the power and precision of the test results.
Why is random sampling important in hypothesis testing?
To ensure the generalizability of the results.
What are the ethical considerations in hypothesis testing?
Maintaining transparency, avoiding manipulation of data and respecting participant rights.
How does bias affect the results of hypothesis testing?
It can lead to erroneous conclusions and reduce the reliability of the test results.
What is the central limit theorem and why is it important in statistics?
It states that the sample means will approximate a normal distribution as the sample size increases, regardless of the population’s distribution.
How do you choose between using a parametric and a non-parametric test?
Parametric tests assume underlying statistical distributions; non-parametric do not, making them suitable for non-normal data.
Why might a researcher use a two-tailed test instead of a one-tailed test?
To avoid directionality in testing hypotheses, providing a more conservative test.
What strategies can be used to increase the power of a statistical test?
Improving the study design, increasing the sample size, or using more precise measurements.
How does the choice of significance level affect the results of a test?
A lower level reduces Type I errors but increases the chance of Type II errors.
What is the difference between statistical significance and clinical significance?
Statistical significance may indicate a mathematically detected difference, but it might not be meaningful in practical terms.
How do you interpret confidence intervals that do not include zero?
They suggest a significant effect or difference (as zero is not within the interval).
What are common mistakes made during hypothesis testing?
Ignoring assumptions of the test, misinterpreting p-values, or overlooking necessary data transformations.
What is the benefit of reporting confidence intervals along with p-values?
They provide a range of plausible values for the parameter and more information than a single p-value.
How do you handle multiple comparisons in statistical analysis?
Adjusting significance levels or using statistical methods to control for family-wise error rate.
What is the Bonferroni correction and when should it be used?
A method to adjust the significance threshold when multiple comparisons are made, to control the family-wise error rate.
What is the role of replication in hypothesis testing?
Increases confidence in the results by demonstrating reproducibility.
Why is pre-registration important in hypothesis trials?
To minimize biases and confirmatory biases in conducting the research.
What is the consequence of p-hacking?
Leads to an inflated Type I error rate, questioning the reliability of findings.
How do you determine if your test results are robust?
By performing robustness checks such as rerunning the tests with different assumptions or subsets of data.
What are the limitations of the p-value?
It does not provide information about the magnitude or importance of an effect.
What factors should be considered when setting the sample size for a study?
Considering the expected effect size, desired power, and the level of acceptable error.
How can researchers ensure the integrity of their hypothesis testing process?
By adhering to predefined protocols, documenting all decisions made during the research, and conducting peer reviews.
