When Exit Polls Went Awry: Five Instances Where Results Threw Surprises

Exit polls have often provided a glimpse into the voter sentiment, offering a near-accurate prediction of election results before the official count by the Election Commission. However, they have not always been precise.

As the final phase of polling for the Lok Sabha polls will end on June 1, the focus will shift to exit polls. Though these polls gauge the electorate’s mood, they sometimes diverge from the actual outcomes.

Here we will provide instances when the predictions have gone wrong

2004 Lok Sabha Elections

In the 2004 Lok Sabha elections, the BJP, buoyed by state victories in Madhya Pradesh, Chhattisgarh, and Rajasthan, sought early re-election under the 'India Shining' campaign. Exit polls projected 240 to 250 seats for the BJP-led NDA. However, the final results were unexpectedly different.

Delhi Assembly Elections 2015

During the 2015 Delhi Assembly elections, the Aam Aadmi Party (AAP) won 67 out of 70 seats. Exit polls predicted a majority for AAP, but none expected such a sweeping victory. Only one poll hinted at a tally over 50 seats.

Bihar Assembly Elections 2015

The 2015 Bihar assembly elections saw exit polls forecasting a tight race with no clear majority. Contrary to these predictions, the RJD-JDU-Congress coalition won decisively. Lalu Prasad's RJD became the single largest party.

UP Assembly Elections 2017

In the 2017 UP Assembly Elections, exit polls predicted a hung assembly with the BJP as the largest party. Yet, the BJP won over 300 seats, a significant rise from their 47 seats in 2012. This result defied exit poll expectations.

2014 Lok Sabha Elections

During the 2014 Lok Sabha Elections, exit polls anticipated a BJP-led NDA victory but not an outright majority. The actual result was that the NDA won well over 300 seats, with the BJP alone crossing the 272-mark. This outcome was far beyond exit poll forecasts, and the Congress achieved only 44 seats.

While exit polls offer insights, their accuracy varies. Historical data underscores the need for caution when interpreting these predictions.