Statistical Inference Flashcards
(16 cards)
What is the purpose of statistical inference?
To evaluate the role of chance (how sure you are that your estimate represents the true parameter in the population of interest).
This concept highlights the uncertainty inherent in statistical estimates.
True or False: Statistical significance rules out bias or confounding as alternative explanations for observed results.
False
Statistical significance does not eliminate the possibility of bias or confounding factors affecting results.
What are the two approaches to hypothesis testing?
Through p-values or confidence intervals
An example of a confidence interval is a 95% CI.
Fill in the blank: Statistical significance does not rule out _______ or confounding as alternative explanations for observed results.
bias
What is the significance level (alpha) commonly used in p value approach?
0.05
This level indicates a threshold for statistical significance.
What does a p value less than 0.05 suggest?
It suggests that the observed result is unlikely to be due to chance if the null hypothesis is true.The result is interpreted as statistically significant. (Groan - Dr Love)
What is the primary interpretation of a p value?
The probability of observing the result or a more extreme value by chance alone, assuming the null hypothesis is true. (lower the values the less likely)
This interpretation is based on repeated experiments.
What is a limitation of p value?
A small p value does not necessarily mean that the size of the effect is strong.
It indicates statistical significance but not the magnitude of the association.
Fill in the blank: A p value of 0.001 suggests there is a ____ chance of observing a risk ratio of 9.0 by chance alone assuming that smoking is not associated withi lung cancer in the source population.
0.1%
This is based on the example of smokers being 9 times more likely to develop lung cancer.
True or False: A statistically significant association is equivalent to a causal association.
False
Statistical significance does not imply causation.
What is a common misconception about p values?
A p value is the probability that the null hypothesis is true.
In reality, a p value tests the compatibility of the data with the null hypothesis, assuming it is true.
What is a confidence interval (CI)?
A range of possible values (upper and lower bound) within which the true population measure of association lies with a stated level of certainty.
What does a 95% confidence interval indicate?
If the study were repeated 100 times, the 95% CI would contain the true population measure of association 95 out of 100 times.
What happens over unlimited repetitions of a study concerning the 95% CI?
In 95% of the repetitions, the CI would contain the true population measure of association.
What conclusion can be drawn if the 95% CI does not contain the null value?
We conclude that we have a statistically significant effect and reject the null hypothesis.
Not sure how to interpret this
