Deck 3: The Phoenix of Statistics

Question 1

A sports nutritionist undertook a study to examine the impact of a reduction in protein intake and team performance, which showed a statistical significance between reduced protein intake and increased team performance. How can she explain to her manager that this does not mean she should start removing protein from the team's diet?

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.

Answer 1

A

Question 2

A sports student conducted a Bayesian analysis of time spent watching sports and improved academic performance. He calculated a Bayes factor of 1. Should he use time spent watching sports as a predictor of academic performance?

A) No, a Bayes factor of 1 suggests that academic performance will not improve with greater time spent watching sports.
B) Yes, a Bayes factor of 1 suggests that there will be a small increase in academic performance with greater time spent watching sports.
C) No, a Bayes factor of 1 suggests that the data is corrupted.
D) Yes, a Bayes factor of 1 suggests that there will be a large increase in academic performance with greater time spent watching sports.

Answer 2

A

Question 3

A customer services strategy manager for a national wellbeing and fitness company is interested in assessing customer usage of their gyms across 20 sites nationally. Different gym managers have collected and analysed data from each of the sites but the resultant twenty reports showed differing p-values, some sites found a statistical significance between increased opening hours of gyms and usage and others did not. Which of the following would it be useful for her to review?

A) Levels of missing data
B) Confidence intervals
C) Outliers
D) The Null Hypotheses

Answer 3

B

Question 4

A new member of a basketball coaching staff, whose team is at the bottom of the league, has just completed a study into factors that affect player performance levels. However, he finds only one statistically significant factor, which he includes in his report but deliberately, omits the other six non-significant findings. What is the term for what the coach has done?

A) p-hacking
B) HARKing
C) Meta analysis
D) Bayesian analysis

Question 5

In our previous example, the human resources manager had already calculated the probability of no girls joining her programme based on sector wide data. In the Bayesian approach, what sort of probability is this?

A) Posterior probability
B) Prior probability
C) Positive probability
D) Inferior probability

Question 6

A manager of an inclusive sports programme in schools was concerned about the lack of girls being recruited on to the programme. There were thirty places on the programme and fifty children had applied, of which only ten were girls. Theoretically, all the children in the participating schools have an equal probability of being recruited as they all match the selection criteria, i.e. they are children at a participating school. However, the manager has data that suggests that boys are more likely to join school based sports programmes than girls based on data from across the school sports programme nationally and from within her own programme historically. However the manager has heavily promoted this initiative, specifically targeting girls and therefore wants to determine the probability that still fewer girls than boys will join. What formula could she use to determine this probability?

A) Bayes' theorem.
B) NHST
C) Pearson's r
D) Cronbach's Alpha

Question 7

A sports researcher wanted to assess the likelihood that girls score more goals than boys do in five a-side soccer. She conducted one study where the probability of making a Type I error was 0.05 and a Type II error was 0.2. Does her research have empirical probability?

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 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 above 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.

Question 8

Your CEO has followed your advice and now wants you to measure effect sizes. You report a Pearson's r of 0.50 for the impact of Power Nylon shorts on athletic performance times. Your CEO wants to know if this is bad, as she remembers that a p-value of 0.30 is not good. What do you tell her?

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. Power Nylon shorts should cease production.
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 roll-out the launch of Power Nylon shorts.
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 launch of Power Nylon shorts 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 roll-out the launch of Power Nylon shorts.

Question 9

A swim coach is assessing swimmers' satisfaction with a new training regime. He had a sample size of 22 and a p-value of 0.3. Does the coach recommend that the swimmers stop using the new training regime?

A) Yes, because the sample has low confidence levels.
B) No, because the sample size is large and therefore the p-values are accurate.
C) Yes, because statistical significance has nothing to do with sample size.
D) No, because the sample size is small and p-values are easily affected by sample size.

Question 10

Your sports and exercise lecturer has devoted the past ten weeks to teaching you the Bayesian approach and is now asking that you offer a critique of it. What key criticism could you raise?

A) The reliance on a prior probability is overly subjective and therefore can be open to a researcher's degrees of freedom.
B) The reliance on a prior probability is overly objective and therefore not open to a researcher's degrees of freedom.
C) The lack of reliance on a null hypothesis is overly objective and therefore open to a researcher's degrees of freedom.
D) The lack of reliance on a prior probability is overly subjective and therefore can be open to a researcher's degrees of freedom.

Question 11

You have just joined the performance modelling team for a national elite sports programme. Your boss has decided that from now on the team will adopt a Bayesian approach. However, not all staff understand what this is; your boss asks you to present a training session. How would you explain a Bayesian approach in your session introduction?

A) An approach where you do not modify 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 that allows you to update the likelihood of your statistical model as more data is collected.
D) An approach where you reject your statistical model once data is collected.

Question 12

Which of the following is not a factor in data analysts' over-use of p-values and NHST in sports and exercise research?

A) Pressure to get a significant result that coaches and managers can easily understand and apply.
B) Time constraints within research encourages quick results.
C) Statistical software encourages the over-use of p-values.
D) Company and team bonus structures incentivise 'results'.

Question 13

You are the CEO of a sports performance forecasting company. You have decided to adopt a Bayesian approach to data analysis and modelling. When you announce this new policy, your staff are unhappy and unconvinced, as they are used to a NHST approach. You stress that the Bayesian approach has several key advantages, including which of the following.

A) You can reject null hypotheses without any data collection.
B) You can evaluate statistical significance using p-values.
C) You can evaluate the likelihood of the null hypothesis being true.
D) You can evaluate complex statistical models without data.

Question 14

A researcher working in a sports performance lab was interested in gender and perception of pain during extreme sports and so conducted a t-test. The mean for males was 66.25 and the mean for females was 78.24, with both groups having a standard deviation of 7. What is the effect size using Cohen's d?

A) 1.712
B) 0
C) 1.7
D) -1.712

Question 15

You lead a product-testing unit for a large sports nutrition company. Your team has conducted forty trials of a new energy supplement but you are not sure if the results are conclusive enough to urge the company to start producing it. A new data analyst has joined your team suggesting that meta-analysis might be a good idea, do you agree?

A) No, because the forty trials were identical and tested the same research question we cannot calculate an average significance for the new supplement.
B) No, because the forty trials were identical and tested the same research question we cannot calculate an average effect size for the new supplement.
C) Yes, because the forty trials were identical and tested the same research question and therefore we can calculate an average significance for the new supplement.
D) 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 supplement.

Question 16

You work for a performance sports clothing company. Your CEO has just read a book on criticisms of the NHST and worries that all company data analysis is now flawed and will lead to huge financial losses. How might you reassure her?

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.