Question 1

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

Accepted Answer

The HR manager should review the confidence intervals across each of the twenty sites as this will give an indication of the strength and accuracy of the results. This will allow the HR manager to compare and contrast the findings across the various sites and determine whether there is an overall effect of gym opening hours on employee usage. Reviewing the null hypothesis, outliers, and levels of missing data may also be useful, but they are not as directly relevant to the differences in p-values across the sites.

Question 2

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

Accepted Answer

The Bayesian approach allows for the evaluation of the likelihood of the null hypothesis being true, which is not possible with the NHST approach. The other options listed are not advantages of the Bayesian approach.

Question 3

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

Accepted Answer

A significant result indicates a strong relationship between two variables, but it does not necessarily indicate that the relationship is important or that changes should be made based on the findings alone. The researcher should conduct further analysis and consider other factors before recommending any changes to lunch break lengths.

Question 4

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

Accepted Answer

The best choice is Bayes' theorem in this scenario, as it is a statistical tool that allows the HR manager to update her beliefs (based on data about the likelihood of men being hired) with new evidence (the number of men and women candidates). Bayes' theorem can help her determine the probability that no women will be hired taking into account the prior probability (based on industry data) and the current evidence (the number of male and female candidates). This can provide insight into the impact of unconscious bias in her company's recruitment process. Pearson's r and Cronbach's Alpha are both measures of reliability or correlation, and NHST is a hypothesis testing approach, but none of them are directly applicable to this scenario.

Question 5

Which of the following is not a factor in data analysts' over-use of p-values and NHST in business and marketing research?
A)Statistical software encourages the over-use of p-values.
B)Time constraints within research encourages quick results.
C)Pressure to get a significant result that CEOs can easily understand and apply.
D)Company bonus structures incentivise 'results'.

Accepted Answer

While statistical software may make it easier to compute p-values and conduct NHST, it is not a major factor in data analysts' over-use of these methods. The other options (B, C, and D) are all important factors that contribute to this issue.

Question 6

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

Accepted Answer

Cohen's d formula is:

d= (M1-M2) / SDpooled

where M1 is the mean of group 1, M2 is the mean of group 2, and SDpooled is the pooled standard deviation of both groups.

In this case:

M1 = 66.25
M2 = 78.24
SDpooled = 7

So,

d = (78.24 - 66.25) / 7
d = 1.712

Therefore, the effect size using Cohen's d is 1.712, which is best represented by choice C.

Question 7

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.

Accepted Answer

Meta-analysis can be used to calculate an average effect size for the new drug based on the results of the forty identical trials. Significance cannot be calculated through meta-analysis as it is dependent on the specific sample size and design of each individual trial.

Question 8

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

Accepted Answer

The probability calculated by the human resources manager based on sector wide data is a prior probability. In Bayesian approach, prior probability is the probability assigned to an event before any evidence is collected.

Question 9

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 where you reject your statistical model once data is collected.
B)An approach where you do not modify the likelihood of your statistical model as more data is collected.
C)An approach that allows you to focus on testing the null hypothesis based on data collection.
D)An approach that allows you to update the likelihood of your statistical model as more data is collected.

Accepted Answer

A Bayesian approach involves updating the likelihood of your statistical model as more data is collected. This allows for a more flexible and adaptive approach to modeling, as opposed to simply accepting or rejecting a fixed model. Bayesian methods incorporate prior beliefs or knowledge into the analysis and update these beliefs based on new data.

Question 10

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 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.
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 small effect,suggesting she should stop the launch of Unblock Me Now until more data analysis is done.
C)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.
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 large effect,suggesting she should rollout the launch of Unblock Me Now.

Accepted Answer

The answer of Your CEO has followed your advice and...

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

Accepted Answer

The researcher should not make a recommendation based solely on the p-value and small sample size. The p-value can be easily affected by sample size, and with a small sample size, the results may not be representative of the entire population. Therefore, the researcher should consider a larger sample size or additional data before making any recommendation.

Question 12

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.

Accepted Answer

Empirical probability refers to the probability obtained through repeated identical experiments. As mentioned in option B, 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. Therefore, the answer is B.

Question 13

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

Accepted Answer

The data analyst has engaged in p-hacking, which involves manipulating data or statistical analyses to achieve a desired result, such as finding a significant effect where there may actually be none. By omitting the non-significant factors, the data analyst is selectively reporting results to support a predetermined conclusion, which is a form of p-hacking.

Question 14

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.

Accepted Answer

The Bayesian approach relies on a prior probability which is determined subjectively by the researcher. This means that the results can be influenced by the researcher's own biases and degrees of freedom. In contrast, other statistical approaches like the frequentist approach rely on null hypotheses which are more objective and less prone to researcher bias.

Question 15

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

Accepted Answer

A Bayes factor of 1 indicates that the evidence for or against the hypothesis is equally likely under both the null and alternative hypotheses. Therefore, there is not sufficient evidence to support the relationship between skirt length and stock market growth. Hence, it is not worth investing in the stock market based on skirt length variations.

Question 16

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.

Accepted Answer

While NHST does have its flaws, incorporating effect sizes into our analysis can help increase our confidence in the research outputs. It is important to acknowledge the limitations of NHST while also understanding that it is still widely used in the field. Simply dismissing the concerns raised in the book or blindly trusting NHST without any examination of its limitations is not a viable solution.

Quiz 3: The Phoenix of Statistics

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?

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

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?

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?

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 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?

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 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?

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?