- (Topic 1)
A data scientist is performing statistical analysis and is interested in graphically depicting the data set according to the associated quartiles Minimum, First Quartile, Median, Second Quartile, Third Quartile. Which technique would allow for the display of this statistical five number summary?
Correct Answer:
D
A box plot is the technique that would allow for the display of the statistical five number summary, because it is a technique that shows the distribution of a data set using a rectangular box and whiskers. A box plot can help the data scientist visualize the minimum, maximum, median, first quartile, and third quartile of the data set, as well as any outliers or skewness. A box plot can also help the data scientist compare the variation and symmetry of different groups or categories of data. Options A, B, and C are not suitable for displaying the statistical five number summary, because they are techniques that show the frequency, relationship, or density of the data, but not the quartiles or outliers. References:
•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3: Analyze Data
•Understanding the Guide to Business Data Analytics, page 18
•16 Best Types of Charts and Graphs for Data Visualization [+ Guide]
- (Topic 1)
An analytics team is interested in reviewing the results of a public opinion poll that is going to be conducted at the end of the month. One of the factors the team is interested in, is ensuring the result set is statistically significant. Why would this factor be important to the team?
Correct Answer:
D
Ensuring the result set is statistically significant is important to the team because it means that the difference or relationship observed in the data is unlikely to be due to chance or sampling error. Statistical significance helps the team to assess the validity and reliability of their findings, and to draw meaningful conclusions and recommendations from the data.
Statistical significance also helps the team to communicate their results with confidence and credibility to the stakeholders and decision makers12 References: 1: An Easy Introduction to Statistical Significance (With Examples) - Scribbr 2: Statistical Significance in Experimentation and Data Analysis - All About Circuits
- (Topic 2)
While formulating the results from completed analysis, the analytics team is applying different techniques to determine an optimal solution to the specified business problem. Which of the following runs the risk of introducing bias in their decision making process?
Correct Answer:
B
Expert judgement and experience are valuable sources of knowledge and insight for business data analytics, but they can also introduce bias in the decision making process. Bias is a tendency to favor or reject a certain perspective, outcome, or solution based on personal or subjective preferences, beliefs, or expectations. Bias can affect the quality, validity, and reliability of the data analysis and the resulting decisions. Some examples of bias that can affect expert judgement and experience are confirmation bias, availability bias, anchoring bias, and overconfidence bias. To avoid or minimize bias, business data analysts should apply critical thinking, data literacy, and ethical principles throughout the data analysis process. They should also seek diverse perspectives, challenge assumptions, validate findings, and communicate uncertainties and limitations. References:10 Cognitive Biases in Business Analytics and How to Avoid Them; Business Data Analytics: A Decision-Making Paradigm, page 8; Guide to Business Data Analytics, page 11.
- (Topic 2)
The results for a certification exam were revealed in percentage and percentile. How would you infer the results for an attendee at: 75%, 90th percentile?
Correct Answer:
D
A percentage is a way of expressing a number as a fraction of 100, while a percentile is a way of expressing a number as a rank or position in a distribution of values. A percentage tells us how much of something there is, while a percentile tells us how well something performed compared to others. To infer the results for an attendee at 75%, 90th percentile, we need to understand what these two numbers mean.
✑ 75% means that the attendee scored 75 out of 100 possible points on the exam.
This is the absolute score of the attendee, which does not depend on how others performed.
✑ 90th percentile means that the attendee scored higher than 90% of all the attendees who took the exam. This is the relative score of the attendee, which depends on how others performed. For example, if there were 1000 attendees, the 90th percentile would mean that the attendee scored higher than 900 attendees, and lower than 100 attendees.
Therefore, the correct inference is that while the attendee??s exam score was 75/100, the attendee did better than 90% of the attendees. This means that the attendee??s score was above average, and that the exam was relatively difficult or had a low pass
rate. References:
✑ Difference Between Percentage and Percentile | Major Differences - BYJU??S, BYJU??S, accessed on January 20, 2024.
✑ Difference Between Percentage and Percentile (with Examples and Comparison Chart) - Key Differences, Key Differences, accessed on January 20, 2024.
✑ Certification in Business Data Analytics (IIBA ® - CBDA), IIBA, accessed on January 20, 2024.
