Deck 3: The Phoenix of Statistics

Question 1

A researcher in a Human Resources Unit presented a recent study, which showed a statistical significance between length of staff lunch breaks and low productivity; how can she explain to her manager that this does not mean that the length of staff lunch breaks should be reduced?a.

A)A significant result means that the effect is strong.
B)A significant result means that the effect is not relevant.
C)A significant result does not mean that the effect is important.
D)A significant result means that the effect is weak.

Answer 1

C

Question 2

You have just joined the sales modelling team for a start-up software company. 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 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.

Answer 2

A

Question 3

A stockmarket trader conducted a Bayesian analysis of variations in skirt length and stock market growth. He calculated a Bayes factor of 1. Should he use skirt length as a predictor of stock market growth?

A)Yes, a Bayes factor of 1 suggests that it is worth a small investment in the stock market based on skirt length variations.
B)No, a Bayes factor of 1 suggests that it is not worth investing in the stock market based on skirt length variations.
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 the stock market based on skirt length variations.

Answer 3

B

Question 4

A trainee data analyst for a large social media company, which has falling site usage, has just completed a study into factors that affect site users' satisfaction levels. However, he finds only one statistically significant factor, which he includes in his report but he deliberately, omits the other six non-significant findings. What is the term for what the data analyst has done?

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

Question 5

Your business studies 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 objective and therefore not open to a researcher's degrees of freedom.
B)The reliance on a prior probability is overly subjective and therefore can be 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 6

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 Unblock Me Now drain cleaner on reducing drain blockage time. 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.Unblock Me Now 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 rollout the launch of Unblock Me Now.
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 Unblock Me Now 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 rollout the launch of Unblock Me Now.

Question 7

A recruitment analyst wanted to examine the likelihood that advertising on social media is more effective than in print media for recruiting the best candidates. 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 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.

Question 8

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 everyone else uses it, therefore we should.
B)NHST is a flawless approach and the book was probably written by a disciple of the Bayesian approach.
C)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.
D)NHST is a flawless approach and we need to invest in more data analysts who are trained in it.

Question 9

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

Question 10

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

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)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.
C)No, because the forty trials were identical and tested the same research question we cannot 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.

Question 11

A researcher was assessing customer satisfaction with MakeMebeautiful, a new beauty product. He had a sample size of 75 and a p-value of 0.10. Does the researcher recommend that the company stop promoting this product?

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

Question 12

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

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

Question 13

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

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

Question 14

A human resources manager in the IT sector was concerned about unconscious bias in recruitment panels. There were two posts and seven candidates, four men and three women. Theoretically, all the candidates have an equal probability of being hired as they all match the selection criteria. However, the manager has data that suggests that it is more likely men will be hired based on data from across the IT sector and within her own company. However, the manager has implemented many equality initiatives within her company and therefore wants to determine the probability that still no women will be hired. What formula could she use to determine this probability and assess the impact of unconscious bias in her company's recruitment?

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

Question 15

A researcher working in a Human Resources department was interested in gender and sales figures so he 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 16

An HR manager was interested in employee use of company on-site gyms across twenty sites. Different researchers collected and analysed data across each of the sites but the resultant twenty reports showed differing p-values, some sites found a statistical significance between opening hours of on-site gyms and employee usage and others did not. Which of the following would it useful for her to review?

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