Validity is the extent to which a measure or a question accurately captures the intended concept or construct1. The question “What is your salary?” is not a valid measure of job satisfaction, as it does not reflect the various aspects of job satisfaction, such as work environment, recognition, autonomy, growth, etc. Salary is only one possible factor that may influence job satisfaction, but it is not a direct or comprehensive indicator of it23. Therefore, the question is not valid for measuring job satisfaction. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 302: Job Satisfaction: Application, Assessment, Causes, and Consequences, Paul E. Spector, 1997, p. 23: Job Satisfaction Survey, 1.

Weighting is a technique that assigns different values or weights to different records or variables in a dataset, based on their importance or relevance. Weighting can be used to handle missing values by giving them a lower weight or imputing them with a weighted average of other values. Weighting can also help to adjust for sampling bias or non-response bias in the data collection process. References:

• Understanding the Guide to Business Data Analytics, page 16

• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3: Analyze Data

• CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 4

Variation is the degree to which the data values differ from each other or from a central tendency measure, such as the mean or median. Variation can affect the data quality across cities, as it can indicate the presence of outliers, errors, noise, or inconsistency in the data collection or processing methods. Variation can also influence the statistical analysis and interpretation of the results, as it can affect the significance, confidence, and validity of the findings12. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 302: Statistics for Business and Economics, David R. Anderson et al., 2014, p. 83.

According to the Guide to Business Data Analytics, a study population is the subset of the population that meets the eligibility criteria for the research question. A study population should be representative of the population of interest and relevant to the business problem or opportunity. In this situation, the survey will establish a poor study population because the students from local colleges and universities within a 50km radius may not reflect the characteristics, preferences, and behaviours of the potential online customers for the fifty-year-old brick and mortar business. The students may have different demographics, income levels, shopping habits, and needs than the target market for the business. Therefore, the survey results may not be generalizable or applicable to the population of interest and may not provide valid and reliable insights for predicting future sales.

References :  Guide to Business Data Analytics , page 48-49;  CBDA Exam Blueprint , page 7;  Population vs. Sample | Definitions, Differences & Examples - Scribbr

According to the Guide to Business Data Analytics, one of the steps in conducting business data analytics is to identify the research questions that will guide the analysis and help answer the business problem or opportunity. The research questions should be relevant, specific, measurable, achievable, and testable. In this situation, the business problem is the delay in customer payments and the potential solution is to offer a discount. A valid question to ask in this situation is whether discounts have been offered before, and if so, what was the effect on customer behavior and profitability. This question is relevant because it can help assess the feasibility and effectiveness of the proposed solution. It is also specific, measurable, achievable, and testable, as it can be answered by collecting and analyzing historical data on customer payments and discounts.

References :  Guide to Business Data Analytics , page 47-48;  CBDA Exam Blueprint , page 7; [Introduction to Business Data Analytics: A Practitioner View], page 15.