Audit Selection Report for Thompson School District R2-J Director District D (2 Years)

Proportion of the total number of votes for this contest that were cast within this county: 0.4%

See additional statistical information below...

The audit units are reported for selection in priority order, based on the thresholds below, and the "Sum of Square Roots" pseudorandom number generator using the random seed value 569985350872703 combined with the Batch sequence numbers.

Select the top 0 audit units.

Batch Seq Threshold Random Priority Type Batches Ballots Contest Ballots Leslie Young Over Under
Totals 29 0 39
000025 0.011457 0.025277 0.453264 AB p025_mb_562 150 1 0 0 1
000272 0.011457 0.033268 0.344391 AB p272_mb_698 150 1 1 0 0
000114 0.022783 0.093261 0.244297 AB p114_mb_695 150 2 1 0 1
000277 0.033980 0.195097 0.174167 AB p277_mb_653 150 3 0 0 3
000162 0.011457 0.079993 0.143229 AB p162_mb_655 150 1 0 0 1
000275 0.011457 0.092784 0.123483 AB p275_mb_519 150 1 0 0 1
000042 0.011457 0.112267 0.102054 AB p042_mb_631 150 1 0 0 1
000244 0.011457 0.114911 0.099706 AB p244_mb_543 150 1 0 0 1
000169 0.011457 0.131133 0.087372 AB p169_mb_642 150 1 1 0 0
000179 0.011457 0.141037 0.081236 AB p179_mb_540 150 1 1 0 0
000105 0.022783 0.314941 0.072342 AB p105_mb_536 150 2 0 0 2
000113 0.011457 0.167596 0.068363 AB p113_mb_694 150 1 0 0 1
000092 0.011457 0.171830 0.066678 AB p092_mb_527 150 1 0 0 1
000027 0.011457 0.204039 0.056152 AB p027_mb_532 150 1 1 0 0
000064 0.011457 0.211245 0.054237 AB p064_mb_686 150 1 0 0 1
000066 0.011457 0.218066 0.052540 AB p066_mb_691 150 1 1 0 0
000069 0.033980 0.648126 0.052427 AB p069_mb_341 150 3 1 0 2
000282 0.022783 0.469012 0.048577 AB p282_mb_488 150 2 1 0 1
000018 0.022783 0.527893 0.043159 AB p018_mb_530 150 2 2 0 0
000082 0.011457 0.291195 0.039346 AB p082_mb_535 150 1 0 0 1
000223 0.022783 0.596394 0.038202 AB p223_mb_356 150 2 0 0 2
000009 0.011457 0.317568 0.036078 AB p009_mb_571 150 1 1 0 0
000346 0.033980 0.951514 0.035711 AB p346_mb_382 150 3 1 0 2
000050 0.011457 0.326694 0.035070 AB p050_mb_579 150 1 0 0 1
000126 0.011457 0.383868 0.029847 AB p126_mb_318 150 1 0 0 1
000088 0.022783 0.768779 0.029636 AB p088_mb_506 150 2 1 0 1
000010 0.022783 0.774266 0.029426 AB p010_mb_572 150 2 1 0 1
000266 0.011457 0.395883 0.028941 AB p266_mb_634 150 1 0 0 1
000214 0.022783 0.788070 0.028910 AB p214_mb_305 150 2 0 0 2
000023 0.011457 0.418577 0.027372 AB p023_mb_564 150 1 1 0 0
000383 0.022783 0.870264 0.026180 AB p383_mb_253 150 2 0 0 2
000185 0.011457 0.461370 0.024833 AB p185_mb_689 150 1 1 0 0
000048 0.011457 0.480489 0.023845 AB p048_mb_546 150 1 0 0 1
000186 0.011457 0.488840 0.023438 AB p186_mb_696 150 1 0 0 1
000036 0.011457 0.490256 0.023370 AB p036_mb_557 150 1 1 0 0
000176 0.011457 0.507949 0.022556 AB p176_mb_637 150 1 0 0 1
000033 0.011457 0.552539 0.020736 AB p033_mb_554 150 1 1 0 0
000083 0.011457 0.622839 0.018395 AB p083_mb_521 150 1 1 0 0
000112 0.011457 0.684901 0.016728 AB p112_mb_693 150 1 1 0 0
000074 0.011457 0.685019 0.016726 AB p074_mb_335 150 1 0 0 1
000057 0.011457 0.705503 0.016240 AB p057_mb_662 150 1 1 0 0
000108 0.011457 0.729251 0.015711 AB p108_mb_514 150 1 0 0 1
000203 0.011457 0.732381 0.015644 AB p203_mb_313 131 1 1 0 0
000389 0.011457 0.754972 0.015176 AB p389_mb_428 150 1 0 0 1
000262 0.011457 0.774803 0.014787 AB p262_mb_664 150 1 1 0 0
000173 0.011457 0.815687 0.014046 AB p173_mb_645 150 1 0 0 1
000081 0.011457 0.854479 0.013409 AB p081_mb_470 150 1 1 0 0
000072 0.011457 0.855452 0.013393 AB p072_mb_338 74 1 1 0 0
000213 0.011457 0.862405 0.013285 AB p213_mb_376 150 1 1 0 0
000075 0.011457 0.914968 0.012522 AB p075_mb_334 118 1 1 0 0
000148 0.011457 0.963496 0.011891 AB p148_mb_463 150 1 0 0 1
000059 0.011457 0.971916 0.011788 AB p059_mb_699 150 1 1 0 0
000320 0.011457 0.996435 0.011498 AB p320_mb_395 150 1 1 0 0

Audit statistics

The "Number of audit units to audit" is based on the NEGEXP method, which is very efficient and requires selecting larger audit units with higher probability than smaller ones. The numbers given here are based on a confidence level of 75%. I.e. they are designed so the audit will either 1) find a discrepancy and call for an escalation or full hand recount, or 2) reduce the risk of confirming an incorrect outcome to (100 - 75)%, even if the tally system has been manipulated. A maximum "within-precinct-miscount" of 20% is assumed. See On Auditing Elections When Precincts Have Different Sizes, by Javed A. Aslam, Raluca A. Popa and Ronald L. Rivest


Contest: Thompson School District R2-J Director District D (2 Years)

Number of precincts: 53
Total number of votes cast:  68
Average number of votes/precinct: 1.28301886792
Median number of votes/precinct: 1
Maximum number of votes/precinct: 3
Minimum number of votes/precinct: 1
Ratio of max/min: 3.0
margin =  70.766 percent, 48.12088 votes
s =  0.2  (maximum within-precinct-miscount)
alpha =  0.25  (confidence is 1 - alpha:  0.75 )

Rule of Thumb says:
    0.783593455117 precincts.
    expected workload =  2.56603773585 votes counted.

APR says:
    b = 93.76495 precincts needed to hold corruption
    u =  1 precincts to audit
    expected workload =  1.28301886792 votes
    confidence level to find one of b =  1.76915
    bmin = 53
    confidence level to find one of bmin =  1.0

SAFE says:
    bmin = 53
    Number of precincts to audit = u = 1
    Confidence level achieved =  1.0
    expected workload =  1.28301886792

Negexp says:
    w = 34.7118774696
    largest probability =  0.0339795734444
    smallest probability =  0.0114572927064
    expected number of precincts audited =  0.776737564003
    expected workload =  1.18566484738 votes counted

PPEBWR says:
    t =  1
    largest total probability =  0.0441176470588
    smallest total probability =  0.0147058823529
    expected number of precincts audited = 1.0
    expected workload =  1.52941176471 votes counted.
    max difference from negexp =  0.0101380736145