{"columns":[{"alerts":[{"code":"long_tail","level":"info","message":"49 singleton categories"}],"column":"NAME","extras":{"singletons":49,"top_values":[["California",1],["Texas",1],["Florida",1],["New York",1],["Pennsylvania",1],["Ohio",1],["Illinois",1],["Georgia",1],["North Carolina",1],["Michigan",1],["Virginia",1],["Washington",1],["Arizona",1],["Massachusetts",1],["Tennessee",1],["Indiana",1],["Missouri",1],["Maryland",1],["Wisconsin",1],["Colorado",1]]},"kind":"categorical","n":49,"n_null":0,"n_unique":49,"null_rate":0.0,"stats":{"cardinality":49,"entropy":5.614709844115208,"entropy_ratio":1.0,"top_rate":0.02040816326530612,"top_value":"California"}},{"alerts":[{"code":"long_tail","level":"info","message":"49 singleton categories"}],"column":"state","extras":{"singletons":49,"top_values":[["California",1],["Texas",1],["Florida",1],["New York",1],["Pennsylvania",1],["Ohio",1],["Illinois",1],["Georgia",1],["North 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rows beyond 1.5 IQR"}],"column":"veteran_percentage","extras":{"histogram":{"counts":[45,3,0,0,0,0,1],"edges":[0.22,39.76714285714285,79.3142857142857,118.86142857142855,158.4085714285714,197.95571428571427,237.5028571428571,277.05]},"sample":[4.85,1.88,1.33,6.93,7.19,5.08,6.63,4.34,3.87,1.32,32.17,7.14,8.27,13.52,1.43,53.14,0.78,0.22,1.48,7.77,4.12,6.56,7.83,44.97,31.03,8.93,0.62,7.03,6.09,1.12,3.82,4.31,2.73,5.8,1.44,24.23,6.25,0.48,46.42,3.24,37.8,2.34,1.7,277.05,0.76,16.99,0.36,5.18,2.53]},"kind":"numeric","n":49,"n_null":0,"n_unique":49,"null_rate":0.0,"stats":{"iqr":6.13,"kurtosis":34.86117220169976,"max":277.05,"mean":14.919795918367347,"median":5.08,"min":0.22,"n_outliers":8,"outlier_rate":0.16326530612244897,"q1":1.7,"q3":7.83,"skew":5.788567703056367,"std":40.365920314965564,"zero_rate":0.0}}],"insights":{"errors":[],"insights":[{"confidence":"high","critiques":[],"evidence_keys":["veteran_percentage.stats.skew","veteran_percentage.stats.median","veteran_percentage.stats.max","veteran_percentage.alerts","veteran_percentage.stats.n_outliers","total_population.stats.skew","veteran_population.stats.n_outliers","row_count"],"featured_charts":[{"caption":"Look for the extreme right tail \u2014 a handful of states have veteran percentages far above the median of 5.08, with one reaching 277.05.","column":"veteran_percentage","kind":"histogram"},{"caption":"Compare veteran population counts across states to see which states have the largest absolute veteran communities.","column":"veteran_population","kind":"bar"},{"caption":"Total population varies widely across states, providing context for interpreting differences in veteran counts and percentages.","column":"total_population","kind":"bar"},{"caption":"Ranked by state, this reveals which specific states are driving the outlier veteran percentage values identified in the histogram.","column":"veteran_percentage","kind":"bar"}],"model":"anthropic:default","narrative":"This dataset contains U.S. state-level veteran population statistics for 49 states, including total population, veteran counts, and the percentage of the population that are veterans. The most important signal is in veteran_percentage, which is extremely right-skewed (skew: 5.79) with 8 outliers and a max of 277.05 \u2014 far above the median of 5.08 \u2014 suggesting a small number of states have dramatically elevated veteran shares worth investigating. Total population and veteran population both distribute relatively evenly across states with no outliers, meaning the percentage anomalies are not simply a function of small population size.","scope":"dataset","target":"__global__"},{"confidence":"high","critiques":[],"evidence_keys":["max","median","mean","skew","kurtosis","n_outliers","outlier_rate","q1","q3","null_rate"],"model":"anthropic:default","narrative":"This column represents veteran percentage figures across 49 fully distinct, non-null records \u2014 likely a rate or share (%) of veterans in some population unit such as counties, districts, or organizations. The distribution is severely right-skewed (skew = 5.79, kurtosis = 34.86): the median is only 5.08 while the mean is pulled to 14.92 by extreme outliers. Most strikingly, the maximum value is 277.05, which exceeds 100% and is physically impossible for a percentage, strongly suggesting data quality issues \u2014 either unit errors (e.g., per-thousand rates mixed with percentages) or data entry errors among the 8 flagged outliers (outlier rate = 16.3%).","role":"feature","scope":"column","target":"veteran_percentage","treatment":"Investigate and correct values exceeding 100 before use; consider log-transform or winsorization after cleaning to address skew."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","cardinality","entropy_ratio","top_rate","top_value","top_values"],"model":"anthropic:default","narrative":"This column contains U.S. state names, with 49 unique values across 49 rows and zero nulls \u2014 consistent with a near-complete list of U.S. states (50 states minus one, or possibly including a territory/district instead). Every value appears exactly once (top_rate = 0.0204, i.e., 1/49), yielding a maximum entropy_ratio of 1.0, which flags as a long-tail alert but is actually a perfectly uniform identifier distribution. The missing 50th entry is worth investigating \u2014 one state may be absent or replaced by a non-standard entry.","role":"label","scope":"column","target":"NAME","treatment":"Use as a join key or group-by label; audit for the missing 50th U.S. state before aggregation."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","cardinality","entropy_ratio","top_rate","null_rate","top_values"],"model":"anthropic:default","narrative":"This column contains US state names, with exactly 49 rows and 49 unique values \u2014 meaning every row is a distinct state (notably, only 49 of the 50 US states are present, so one state is missing from the dataset). The entropy ratio of 1.0 and uniform frequency of 1 per state confirm perfect cardinality with zero repetition, making this a de facto identifier rather than a grouping variable. The 'long_tail' alert is technically a misnomer here: all values occur exactly once, so there is no skew whatsoever.","role":"label","scope":"column","target":"state","treatment":"Use as a row label or join key; do not one-hot encode with cardinality 49 without dimensionality reduction \u2014 consider mapping to regions or numeric features instead."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.mean","stats.median","stats.skew","stats.kurtosis","stats.iqr","stats.std","stats.n_outliers"],"model":"anthropic:default","narrative":"This column represents total population counts for 49 distinct geographic units (likely US states or similar administrative regions), with every row populated and fully unique. Values span from 548,984 to 39,227,468 with a mean of ~18.8M and median of ~19.6M, suggesting a reasonably balanced distribution \u2014 the near-zero skew (0.10) and platykurtic kurtosis (-1.16) confirm a flat, spread-out distribution rather than a peaked one. The wide IQR of ~20.4M and std of ~12.2M reflect the natural size heterogeneity across regions, and notably there are zero detected outliers despite this spread.","role":"feature","scope":"column","target":"total_population","treatment":"Use as-is or consider per-capita normalization when combining with other count-based columns."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.skew","stats.kurtosis","stats.n_outliers","stats.iqr","stats.mean","stats.median"],"model":"anthropic:default","narrative":"This column represents the veteran population count for each of 49 geographic units (likely U.S. states or territories, given the count). All 49 rows are unique and non-null, consistent with one record per jurisdiction. The distribution is remarkably symmetric (skew \u2248 -0.04) and platykurtic (kurtosis \u2248 -1.34), meaning values are spread broadly and uniformly across a wide range from 61,090 to 1,786,891 with no outliers \u2014 an unusual flatness for a population variable, which typically skews right.","role":"feature","scope":"column","target":"veteran_population","treatment":"Use as-is or normalize per total state population to create a veteran rate before modelling."}],"providers":["anthropic:default"],"total_usage":{"completion_tokens":1795,"prompt_tokens":4570,"total_tokens":6365}},"language_counts":{},"meta":{"generated_at":"2026-06-22T00:04:14+00:00","mode":"full","row_count":49,"sampled_rows":49,"seed":42,"source":"/home/coolhand/html/datavis/data_trove/demographic/veterans/military_firearm_veterans.csv"},"notes":[],"saturn_version":"0.2.0","schema":{"NAME":"categorical","state":"categorical","total_population":"numeric","veteran_percentage":"numeric","veteran_population":"numeric"}}