AUDITING A PRACTICAL APPROACH
WITH DATA ANALYTICS 2ND EDITION
RAYMOND JOHNSON LAURA DAVIS WILEY
ROBYN MORONEY TEST BANK
COMPREHENSIVE TEST PAPER 2026
COMPLETE ANSWERS ACCURATE
⫸ Audit analytics. Answer: The use of data analysis in audits to
increase coverage, identify anomalies, and improve audit effectiveness.
⫸ Four types of data analytics. Answer: Descriptive, Diagnostic,
Predictive, and Prescriptive analytics.
⫸ Descriptive analytics (audit). Answer: Summaries and aggregations
that describe what happened (counts, totals, aging, trends).
⫸ Diagnostic analytics (audit). Answer: Analysis that explains why
something happened (comparisons, drill-downs, segmentation).
⫸ Predictive analytics (audit). Answer: Models that estimate what is
likely to occur (risk scoring, forecasting, anomaly prediction).
⫸ Prescriptive analytics (audit). Answer: Techniques that propose
actions or recommendations based on predicted outcomes.
, ⫸ Box plot. Answer: A graph showing median, quartiles, and outliers
used to evaluate distributions.
⫸ Z-score. Answer: The number of standard deviations a value is from
the mean; used to detect unusual values.
⫸ t-Test. Answer: A hypothesis test comparing means of two groups to
determine whether a difference is statistically significant.
⫸ Benford's Law. Answer: A pattern describing expected leading-digit
frequencies in naturally occurring datasets.
⫸ Using Benford's Law to detect fraud. Answer: Compare actual
leading-digit frequencies to Benford expectations; large deviations may
indicate manipulation.
⫸ Fuzzy match. Answer: A technique to identify text values that are
similar but not identical (e.g., misspelled vendor names).
⫸ Inner join. Answer: Returns only rows where keys match in both
tables.
⫸ Left join. Answer: Returns all rows from the left table plus matches
from the right; unmatched right values are null.
WITH DATA ANALYTICS 2ND EDITION
RAYMOND JOHNSON LAURA DAVIS WILEY
ROBYN MORONEY TEST BANK
COMPREHENSIVE TEST PAPER 2026
COMPLETE ANSWERS ACCURATE
⫸ Audit analytics. Answer: The use of data analysis in audits to
increase coverage, identify anomalies, and improve audit effectiveness.
⫸ Four types of data analytics. Answer: Descriptive, Diagnostic,
Predictive, and Prescriptive analytics.
⫸ Descriptive analytics (audit). Answer: Summaries and aggregations
that describe what happened (counts, totals, aging, trends).
⫸ Diagnostic analytics (audit). Answer: Analysis that explains why
something happened (comparisons, drill-downs, segmentation).
⫸ Predictive analytics (audit). Answer: Models that estimate what is
likely to occur (risk scoring, forecasting, anomaly prediction).
⫸ Prescriptive analytics (audit). Answer: Techniques that propose
actions or recommendations based on predicted outcomes.
, ⫸ Box plot. Answer: A graph showing median, quartiles, and outliers
used to evaluate distributions.
⫸ Z-score. Answer: The number of standard deviations a value is from
the mean; used to detect unusual values.
⫸ t-Test. Answer: A hypothesis test comparing means of two groups to
determine whether a difference is statistically significant.
⫸ Benford's Law. Answer: A pattern describing expected leading-digit
frequencies in naturally occurring datasets.
⫸ Using Benford's Law to detect fraud. Answer: Compare actual
leading-digit frequencies to Benford expectations; large deviations may
indicate manipulation.
⫸ Fuzzy match. Answer: A technique to identify text values that are
similar but not identical (e.g., misspelled vendor names).
⫸ Inner join. Answer: Returns only rows where keys match in both
tables.
⫸ Left join. Answer: Returns all rows from the left table plus matches
from the right; unmatched right values are null.
