A) NHST
B) Pearson's r
C) Cronbach's Alpha
D) Bayes' theorem.
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Multiple Choice
A) Positive probability
B) Posterior probability
C) Prior probability
D) Inferior probability
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Multiple Choice
A) -1.712
B) 1.7
C) 0
D) 1.712
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Multiple Choice
A) No, because the sample size is large and therefore the p-values are accurate.
B) Yes, because statistical significance has nothing to do with sample size.
C) No, because the sample size is small and p-values are easily affected by sample size.
D) Yes, because the sample has low confidence levels.
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Multiple Choice
A) Yes, because the forty trials were identical and tested the same research question and therefore we can calculate an average significance for the new drug.
B) No, because the forty trials were identical and tested the same research question we cannot calculate an average effect size for the new drug.
C) Yes, because the forty trials were identical and tested the same research question and therefore we can calculate an average effect size for the new drug.
D) No, because the forty trials were identical and tested the same research question we cannot calculate an average significance for the new drug.
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Multiple Choice
A) Time constraints within research encourages quick results.
B) Pressure to get a significant result that healthcare managers can easily understand and apply.
C) Career bonus structures incentivise 'results'.
D) Statistical software encourages the over-use of p-values.
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Multiple Choice
A) Confidence intervals
B) Levels of missing data
C) Outliers
D) The Null Hypotheses
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Multiple Choice
A) No, a Bayes factor of 1 suggests that although declining stork numbers appear to coincide with declining birth rates it is not 'worth mentioning' and other factors may be at work.
B) Yes, a Bayes factor of 1 suggests that it is worth trying to increase stork numbers in order to increase the birth rate.
C) No, a Bayes factor of 1 suggests that the data is corrupted.
D) Yes, a Bayes factor of 1 suggests that it is worth heavily investing in reducing stork numbers in order to reduce the birth rate.
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Multiple Choice
A) HARKing
B) Meta-analysis
C) p-hacking
D) Bayesian analysis
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Multiple Choice
A) The lack of reliance on a prior probability is overly subjective and therefore can be open to a researcher's degrees of freedom.
B) The lack of reliance on a null hypothesis is overly objective and therefore open to a researcher's degrees of freedom.
C) The reliance on a prior probability is overly objective and therefore not open to a researcher's degrees of freedom.
D) The reliance on a prior probability is overly subjective and therefore can be open to a researcher's degrees of freedom.
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Multiple Choice
A) No, to have empirical probability the likelihood of an effect being detected requires a series of repeated identical experiments, where the probability of making a Type I error is above 0.05 and a Type II error is 0.2.
B) No, to have empirical probability the likelihood of an effect being detected requires a series of repeated identical experiments, where the probability of making a Type I error is 0.05 and a Type II error is 0.2.
C) Yes, to have empirical probability the likelihood of an effect being detected requires a single experiment, where the probability of making a Type I error is above 0.05 and a Type II error is 0.2.
D) No, to have empirical probability the likelihood of an effect being detected requires a single experiment, where the probability of making a Type I error is above 0.05 and a Type II error is 0.1.
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Multiple Choice
A) A significant result does not mean that the effect is important
B) A significant result means that the effect is strong.
C) A significant result means that the effect is not relevant.
D) A significant result means that the effect is weak.
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Multiple Choice
A) An approach that allows you to update the likelihood of your statistical model as more data is collected.
B) An approach that allows you to focus on testing the null hypothesis based on data collection.
C) An approach where you do not modify the likelihood of your statistical model as more data is collected.
D) An approach where you reject your statistical model once data is collected.
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Multiple Choice
A) You tell her that effect size and p-values are the same and that a Pearson's r of 0.50 means there is no statistically significant effect and oily fish supplements should not be given to dementia patients.
B) You tell her that effect size and p-values are not the same and that a Pearson's r of 0.50 is a large effect, suggesting she should encourage the use of oily fish supplements in dementia patients.
C) You tell her that effect size and p-values are not the same and that a Pearson's r of 0.50 is a small effect, suggesting she should stop the use of oily fish supplements in dementia treatments until more data analysis is done.
D) You tell her that effect size and p-values are not the same and that a Pearson's r of 0.50 is a medium effect, suggesting she should encourage the use of oily fish supplements in dementia treatments.
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Multiple Choice
A) You can evaluate statistical significance using p-values.
B) You can evaluate the likelihood of the null hypothesis being true.
C) You can reject null hypotheses without any data collection.
D) You can evaluate complex statistical models without data.
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Multiple Choice
A) NHST does have its flaws but if we incorporate an examination of effect sizes into our analysis, we should be able to trust our research outputs.
B) NHST does have its flaws but everyone else uses it, therefore we should.
C) NHST is a flawless approach and the book was probably written by a disciple of the Bayesian approach.
D) NHST is a flawless approach and we need to invest in more data analysts who are trained in it.
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