1
Who is most likely to...
debug a SAS macro five minutes before a submission deadline?
100 Statistical Programmers Most Likely To Questions for Data and Clinical Trial Teams
Statistical programmers turn complex study data into reliable tables, listings, figures, and analysis results, often while navigating tight deadlines and detailed specifications. That combination creates plenty of memorable habits, technical quirks, and inside jokes for a round of Most Likely To. These statistical programmer questions spotlight everything from SAS macros and R scripts to CDISC standards, validation, and last-minute TLF changes. Gather the team and find out who is most likely to debug the impossible issue or catch the tiniest data discrepancy.
Draw from the deck below or scroll to browse all questions
All 100 Questions
2
Who is most likely to...
spot a one-character error in a complicated R script?
3
Who is most likely to...
build a reusable macro for a task that only takes ten minutes?
4
Who is most likely to...
name every variable in an SDTM domain from memory?
5
Who is most likely to...
forget to update a format catalog before generating tables?
6
Who is most likely to...
create the cleanest ADaM dataset on the project?
7
Who is most likely to...
find a missing parenthesis in a 500-line program?
8
Who is most likely to...
volunteer to review a teammate's validation output?
9
Who is most likely to...
turn a simple frequency table into a full programming challenge?
10
Who is most likely to...
save a permanent dataset with an extremely specific filename?
11
Who is most likely to...
notice that one patient appears twice in a listing?
12
Who is most likely to...
explain a p-value to a nonprogrammer using a coffee analogy?
13
Who is most likely to...
replace a long SAS data step with one elegant PROC SQL query?
14
Who is most likely to...
keep a personal library of useful SAS macros?
15
Who is most likely to...
ask for the shell script instead of running every program manually?
16
Who is most likely to...
catch an unexpected decimal-place change in a results table?
17
Who is most likely to...
generate a perfectly formatted TLF on the first attempt?
18
Who is most likely to...
discover that a study population flag is coded incorrectly?
19
Who is most likely to...
use R to investigate a result that SAS produced unexpectedly?
20
Who is most likely to...
remember which version of a dataset generated a particular output?
21
Who is most likely to...
write a program that passes validation without a single finding?
22
Who is most likely to...
find a hidden warning in a log everyone else ignored?
23
Who is most likely to...
organize a programming directory into perfectly labeled folders?
24
Who is most likely to...
question a specification because the analysis set definition seems unclear?
25
Who is most likely to...
finish a complex table while waiting for the final data transfer?
26
Who is most likely to...
create a macro variable with a name only another programmer understands?
27
Who is most likely to...
compare two datasets with PROC COMPARE before anyone asks?
28
Who is most likely to...
build a custom function to simplify repeated R analysis?
29
Who is most likely to...
recognize a transposed dataset problem from the output alone?
30
Who is most likely to...
check the log before looking at the actual table?
31
Who is most likely to...
catch a format mismatch between a listing and a figure?
32
Who is most likely to...
produce a beautiful forest plot from messy analysis data?
33
Who is most likely to...
insist on using controlled terminology exactly as specified?
34
Who is most likely to...
trace a derived variable back through several dataset transformations?
35
Who is most likely to...
write detailed comments for code that seemed self-explanatory?
36
Who is most likely to...
find an issue in a shell script that launches an entire batch?
37
Who is most likely to...
remember the difference between SDTM and ADaM without checking documentation?
38
Who is most likely to...
create a temporary dataset and accidentally depend on it for the entire analysis?
39
Who is most likely to...
ask whether a missing value is truly missing or intentionally blank?
40
Who is most likely to...
use SQL joins to solve a problem that started in a SAS data step?
41
Who is most likely to...
build a custom R Markdown report for a recurring analysis?
42
Who is most likely to...
identify the source of a shifted column in a production table?
43
Who is most likely to...
finish a validation comparison before the original programmer notices the discrepancy?
44
Who is most likely to...
keep every program aligned with the latest statistical analysis plan?
45
Who is most likely to...
find a duplicated record by checking an unexpected count?
46
Who is most likely to...
turn a vague statistician request into a precise programming specification?
47
Who is most likely to...
explain why a perfectly valid result looks different after rounding?
48
Who is most likely to...
create a backup copy of every dataset before making a major change?
49
Who is most likely to...
notice that a treatment label is inconsistent across several TLFs?
50
Who is most likely to...
use a hash object to solve a difficult lookup problem?
51
Who is most likely to...
write a data-driven macro that handles every study arm?
52
Who is most likely to...
find an uninitialized variable warning hiding in a lengthy SAS log?
53
Who is most likely to...
recognize that a missing record comes from an upstream data issue?
54
Who is most likely to...
build a QC program that uses a completely different programming approach?
55
Who is most likely to...
ask for an independent review of a complicated derivation?
56
Who is most likely to...
generate a listing that answers a question nobody thought to ask?
57
Who is most likely to...
use Git to recover an earlier version of an analysis program?
58
Who is most likely to...
remember the exact reason a particular exclusion flag was created?
59
Who is most likely to...
catch a difference between production and validation environments?
60
Who is most likely to...
create a format for a value that appears only once in the data?
61
Who is most likely to...
translate a statistical method into reliable production code?
62
Who is most likely to...
notice that a confidence interval is inconsistent with the displayed estimate?
63
Who is most likely to...
write a SQL query with more joins than anyone expected?
64
Who is most likely to...
build a parameterized program that works across multiple studies?
65
Who is most likely to...
question an unexpected zero count in a treatment summary?
66
Who is most likely to...
find a typo in a table shell before programming begins?
67
Who is most likely to...
use metadata to automate part of a CDISC mapping task?
68
Who is most likely to...
keep a spreadsheet of every open programming issue?
69
Who is most likely to...
identify a subject-level discrepancy by comparing domains?
70
Who is most likely to...
generate the final submission package with flawless naming conventions?
71
Who is most likely to...
spot that a date conversion changed the analysis population?
72
Who is most likely to...
write a utility program that the whole statistical programming team adopts?
73
Who is most likely to...
notice that a page break makes a regulatory listing difficult to read?
74
Who is most likely to...
create a custom validation report for a high-risk derivation?
75
Who is most likely to...
ask whether an imputation rule matches the SAP before coding it?
76
Who is most likely to...
find a problem caused by character and numeric variables being mixed?
77
Who is most likely to...
use PROC REPORT to produce a table with unusually precise formatting?
78
Who is most likely to...
build an R Shiny tool for checking study data?
79
Who is most likely to...
remember which analysis dataset contains the best source for a derived endpoint?
80
Who is most likely to...
catch a rounding discrepancy between a figure and its source table?
81
Who is most likely to...
organize a peer-review checklist for every new program?
82
Who is most likely to...
investigate why a model failed to converge?
83
Who is most likely to...
write a macro that makes a last-minute shell change painless?
84
Who is most likely to...
recognize a coding issue from a single unusual footnote?
85
Who is most likely to...
compare outputs across SAS versions before a major delivery?
86
Who is most likely to...
create an automated check for missing labels and formats?
87
Who is most likely to...
find that a table title does not match the approved shell?
88
Who is most likely to...
explain the difference between a statistical output and its source dataset?
89
Who is most likely to...
use regular expressions to clean a difficult text variable?
90
Who is most likely to...
track every change made after a data cut?
91
Who is most likely to...
identify a denominator problem in an adverse event summary?
92
Who is most likely to...
build a reproducible workflow from raw data through final TLFs?
93
Who is most likely to...
notice that a parameter code is inconsistent across analysis datasets?
94
Who is most likely to...
write a program that produces both PDF and RTF outputs correctly?
95
Who is most likely to...
question why a result changed after a minor data refresh?
96
Who is most likely to...
spot an unexpected treatment switch in a subject-level analysis dataset?
97
Who is most likely to...
create a clear data flow diagram for a complex derivation?
98
Who is most likely to...
find the exact program responsible for a strange line in a listing?
99
Who is most likely to...
keep an archive of approved shells, specifications, and final outputs?
100
Who is most likely to...
use a controlled macro framework instead of copying code between studies?
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
Statistical programmers have a distinctive mix of technical precision, analytical thinking, and deadline-driven teamwork. Their work involves specialized tools and concepts such as SAS, R, SQL, CDISC, SDTM, ADaM, TLFs, validation, derivations, and regulatory submissions, giving the questions plenty of theme-specific material that general workplace questions cannot capture.
The theme also brings out strong professional personalities. One programmer may be known for immaculate logs, another for clever macros, another for finding discrepancies nobody else sees, and another for creating a perfect QC program under pressure. These questions spark conversations about real projects, memorable data issues, favorite programming techniques, and the inside jokes that develop when a team spends months preparing analysis results.
Best Occasions
These statistical programmer Most Likely To questions are perfect for team lunches, project kickoffs, conference socials, onboarding events, and end-of-study celebrations. They work especially well for groups working in clinical trials, pharmaceutical research, public health, CROs, academic research, and other data-focused environments.
These statistical programmer Most Likely To questions bring technical expertise, project pressure, and workplace humor together in one entertaining game. Use them to celebrate a completed study, welcome a new teammate, or spark conversation at your next team event. The best part is seeing which answers inspire instant agreement, surprised reactions, and stories from past programming challenges.