1
Who is most likely to...
debug a DATA step by reading the log before checking the output
100 SAS Statistical Programmer Most Likely To Questions for Data and Coding Teams
SAS statistical programmers have a special talent for turning messy data into precise, analysis-ready results, often with a little help from DATA steps, PROC procedures, macros, and carefully reviewed logs. These Most Likely To questions celebrate the technical habits, workplace humor, and project experiences that make SAS programming so distinctive. From debugging mysterious notes to defending a perfectly formatted table, these prompts are designed for people who truly understand life in statistical programming.
Draw from the deck below or scroll to browse all questions
All 100 Questions
2
Who is most likely to...
spot a missing semicolon in a long SAS program
3
Who is most likely to...
write a macro to automate a task that only happens twice
4
Who is most likely to...
keep the cleanest SAS programming folder structure
5
Who is most likely to...
forget to clear the WORK library before rerunning a large job
6
Who is most likely to...
recognize a familiar warning in the SAS log immediately
7
Who is most likely to...
build a reusable macro for nearly every project
8
Who is most likely to...
argue that PROC SQL is the best solution for a DATA step problem
9
Who is most likely to...
choose a DATA step when everyone else reaches for PROC SQL
10
Who is most likely to...
find an unexpected duplicate record during a validation check
11
Who is most likely to...
name a dataset with a perfectly consistent naming convention
12
Who is most likely to...
leave a temporary dataset in WORK and wonder where it came from
13
Who is most likely to...
use PROC FORMAT to make a table easier to interpret
14
Who is most likely to...
catch a variable truncation issue before it reaches the final output
15
Who is most likely to...
create a macro variable for a value that could have been typed once
16
Who is most likely to...
explain a complicated SAS program using only the log
17
Who is most likely to...
keep a backup version of every important program
18
Who is most likely to...
discover that a WHERE statement changed the analysis population
19
Who is most likely to...
fix a merge by checking the BY variables first
20
Who is most likely to...
sort a dataset correctly before attempting a DATA step merge
21
Who is most likely to...
use PROC CONTENTS to investigate an unfamiliar dataset
22
Who is most likely to...
find a character-versus-numeric issue during a programming review
23
Who is most likely to...
produce the most polished RTF output from ODS
24
Who is most likely to...
switch from ODS RTF to ODS PDF at the last possible moment
25
Who is most likely to...
remember every option available in PROC REPORT
26
Who is most likely to...
make PROC SQL return exactly the expected number of observations
27
Who is most likely to...
spot an accidental Cartesian join in PROC SQL
28
Who is most likely to...
build a custom format instead of recoding every value
29
Who is most likely to...
protect a program from overwriting a permanent SAS dataset
30
Who is most likely to...
check the SAS log even when the output looks correct
31
Who is most likely to...
explain why a note in the SAS log still matters
32
Who is most likely to...
use PROC COMPARE to settle a disagreement between two outputs
33
Who is most likely to...
find a discrepancy by comparing datasets variable by variable
34
Who is most likely to...
create a shell program for a new clinical study
35
Who is most likely to...
organize analysis datasets according to a study programming standard
36
Who is most likely to...
keep SDTM and ADaM terminology straight during a meeting
37
Who is most likely to...
identify the source of an unexpected treatment arm value
38
Who is most likely to...
trace a derived variable back to its raw data source
39
Who is most likely to...
document every assumption behind a derived analysis variable
40
Who is most likely to...
build an analysis dataset that survives multiple review cycles
41
Who is most likely to...
write a comment explaining a complicated derivation years later
42
Who is most likely to...
validate a table against an independent programming method
43
Who is most likely to...
review a teammate's code without changing the indentation
44
Who is most likely to...
catch an off-by-one issue in a visit window derivation
45
Who is most likely to...
calculate a baseline flag with several competing conditions
46
Who is most likely to...
handle missing values differently for character and numeric variables
47
Who is most likely to...
remember when to use MISSING in PROC SQL
48
Who is most likely to...
use FIRST dot and LAST dot to solve a BY-group problem
49
Who is most likely to...
explain the difference between RETAIN and LAG during a code review
50
Who is most likely to...
find a hidden issue caused by an automatic variable
51
Who is most likely to...
use an ARRAY to simplify repetitive variable processing
52
Who is most likely to...
create a permanent library with the correct LIBNAME statement
53
Who is most likely to...
notice that a library reference points to the wrong project folder
54
Who is most likely to...
recover from a failed LIBNAME connection before asking for help
55
Who is most likely to...
keep study data separate from output datasets
56
Who is most likely to...
build a format catalog that works across multiple programs
57
Who is most likely to...
discover that a user-defined format is missing from an output
58
Who is most likely to...
make a title and footnote match the statistical analysis plan
59
Who is most likely to...
adjust PROC REPORT until every column fits on one page
60
Who is most likely to...
use ODS ESCAPECHAR to solve a table formatting challenge
61
Who is most likely to...
make an output table match a demanding shell specification
62
Who is most likely to...
produce a listing that looks perfect in SAS but strange in PDF
63
Who is most likely to...
find a misplaced label during a table review
64
Who is most likely to...
standardize labels, lengths, and formats across analysis datasets
65
Who is most likely to...
catch a decimal-place error before a clinical study table is delivered
66
Who is most likely to...
program a confidence interval without relying on a copied formula
67
Who is most likely to...
choose the correct statistic in PROC MEANS
68
Who is most likely to...
use PROC FREQ to investigate an unexpected category
69
Who is most likely to...
check a distribution with PROC UNIVARIATE
70
Who is most likely to...
run PROC SUMMARY when PROC MEANS would also work
71
Who is most likely to...
explain why a p-value changed after a small data update
72
Who is most likely to...
reproduce a result from a locked version of the data
73
Who is most likely to...
freeze a final output after the database lock
74
Who is most likely to...
compare a production result with a validation result
75
Who is most likely to...
find that two programmers used different population flags
76
Who is most likely to...
keep track of analysis dataset versions during a study
77
Who is most likely to...
meet a table delivery deadline without skipping quality checks
78
Who is most likely to...
request a revised shell because the original specification is ambiguous
79
Who is most likely to...
spot a mismatch between a table, listing, and figure
80
Who is most likely to...
program a figure with the correct treatment group order
81
Who is most likely to...
use SG procedures to create a clear statistical graphic
82
Who is most likely to...
fix a legend that obscures part of a SAS graph
83
Who is most likely to...
make a Kaplan-Meier plot match the approved template
84
Who is most likely to...
check that censoring symbols appear correctly on a survival plot
85
Who is most likely to...
build a parameter-driven macro for repeated TLF outputs
86
Who is most likely to...
find a macro quoting problem after several failed attempts
87
Who is most likely to...
explain why a macro variable resolves to the wrong value
88
Who is most likely to...
use CALL SYMPUTX to create a macro variable from a dataset
89
Who is most likely to...
avoid hard-coding a study identifier in every SAS program
90
Who is most likely to...
run a batch job and inspect every resulting SAS log
91
Who is most likely to...
schedule a SAS program to run overnight before a deadline
92
Who is most likely to...
keep a list of unresolved log messages for programming review
93
Who is most likely to...
find a note caused by an unintended automatic conversion
94
Who is most likely to...
identify the exact line that caused a SAS error
95
Who is most likely to...
rewrite a slow DATA step to improve runtime
96
Who is most likely to...
reduce a massive intermediate dataset before a PROC SQL join
97
Who is most likely to...
estimate how long a large SAS job will take to finish
98
Who is most likely to...
wait patiently while SAS processes a complicated query
99
Who is most likely to...
save a useful log snippet for a future programming lesson
100
Who is most likely to...
teach a new programmer how to read the SAS log efficiently
Showing 100 questions
Ready to Play?
Use these questions in your next game or create your own custom themed questions
Play Most Likely To OnlineWhy This Theme Works
SAS statistical programmers share a highly specialized world of DATA steps, PROC procedures, macro language, libraries, logs, formats, analysis datasets, and carefully controlled outputs. That shared vocabulary makes the questions instantly recognizable to the group while giving each player a chance to identify the colleague most associated with a particular programming habit.
The theme also brings out inside jokes about missing semicolons, mysterious log notes, stubborn formats, PROC SQL debates, validation findings, and last-minute table changes. Because the prompts reference realistic SAS workflows and statistical programming responsibilities, they encourage stories about debugging, clinical study projects, data reviews, output production, and the small victories that make complex programming work satisfying.
Best Occasions
These SAS Statistical Programmer Most Likely To questions are perfect for team-building events, conference icebreakers, department lunches, onboarding sessions, and end-of-project celebrations. They work especially well for small teams of programmers, biostatisticians, data managers, and clinical research professionals who understand the humor of SAS logs, DATA steps, PROC procedures, and submission deadlines.
For a relaxed game, have everyone point to the colleague who best matches each question. In larger groups, players can write names on cards or vote anonymously. You can also use the questions during a SAS training session, a clinical trial milestone meeting, or a virtual programming team call to spark conversation about workflows, coding habits, and memorable project moments.
These SAS Statistical Programmer Most Likely To questions turn familiar coding routines, validation challenges, and statistical programming habits into an entertaining way to connect with colleagues. Use them at a team event, during a project celebration, or whenever your group needs a break from reviewing outputs. Gather your programmers, start pointing fingers, and see who earns the title of most memorable SAS expert.