ballots at least twice, at the polling place and, starting Monday night after election day, at counting centres. Errors in manual counts Hand-counting can be boring, so officials lose track, or they fail to read their own tally sheets correctly at the end of the process. Average errors in candidate tallies in New Hampshire towns were 2.5% in 2002, including one town with errors up to 20%. Omitting that town cut the average error to 0.87%. Only the net result for each candidate in each town could be measured, by assuming the careful manual recount was fully accurate. Total error can be higher if there were countervailing errors hidden in the net result, but net error in the overall electorate is what determines winners. Connecticut towns in 2007 to 2013 had similar errors up to 2%. Errors were smaller in candidate tallies for precincts in Wisconsin recounted in 2011 and 2016. The average net discrepancy was 0.28% of the recount tally in 2011 and 0.18% in 2016. Errors were 3% to 27% for various candidates in a 2016 Indiana race, because the tally sheet labels misled officials into over-counting groups of 5 tally marks, and officials sometimes omitted absentee ballots or double-counted ballots. India hand tallies paper records from a 1.5% sample of election machines before releasing results. For each voter the machine prints the selected candidate on a slip of paper, displays it to the voter, and drops the slip into a box. In the April–May 2019 elections for the lower house of Congress, the Lok Sabha, the Election Commission hand-tallied the slips of paper from 20,675 voting machines (out of 1,350,000 machines) and found discrepancies for 8 machines, usually of four votes or less. Most machines tally over 16 candidates, and they did not report how many of these candidate tallies were discrepant. They formed investigation teams to report within ten days, were still investigating in November 2019, with no report as of June 2021. Hand tallies before and after 2019 had a perfect match with machine counts. An experiment with multiple types of ballots counted by multiple teams found average errors of 0.5% in candidate tallies when one person, watched by another, read to two people tallying independently. Almost all these errors were overcounts. The same ballots had errors of 2.1% in candidate tallies from sort and stack. These errors were equally divided between undercounts and overcounts