Why VVPATs aren't enough to build confidence in EVMs -Atanu Biswas
Still, doubts about EVMs have been planted, despite the fact that none of the Voter Verifiable Paper Audit Trail (VVPAT) machines showed a mismatch with the EVM count.
Two months after the declaration of Lok Sabha election results, conspiracy theories about possible tampering of Electronic Voting Machines (EVMs) are still doing the rounds. That important opposition leaders have demanded a return to paper ballots and even openly supported EVM-rigging theories has lend credence to the latter – although some of their behavior can be attributed to just being bad losers.
Still, doubts about EVMs have been planted, despite the fact that none of the Voter Verifiable Paper Audit Trail (VVPAT) machines showed a mismatch with the EVM count . The Supreme Court ordered the Election Commission of India (ECI) that five VVPATs per assembly constituency (AC) should be matched with the EVM count of votes. Statistically speaking this is adequate to remove doubts about possible tampering of EVMs. In an earlier article in HT dated April 27, 2018, this author had argued that tallying just 11, 29, 58 and 534 VVPATs per parliamentary constituency (PC) would allow us to find a rigged EVM with 95% probability for scenarios where 25%, 10%, 5% and 0.5% of the EVMs were tampered in a given PC.
Are EVM rigging fears an example of conspiracy theories defeating statistical methods? Ironical as it may sound; an eighteenth century concept in statistics known as Bayes’ theorem can help us understand why matching a sample of VVPATs with EVMs is failing to generate confidence in the credibility of EVMs.
The Bayes’ theorem basically tells us how to calculate the probability of an event given that another event has happened. This is how it works in mathematical terms. If A and B are two events, we know the probabilities of A and B; and also the probability of B given that A has already happened; then Bayes’ theorem can be used to calculate the probability of A given that B has already happened.
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