The Newfoundland and Labrador Election: Where the Polls Missed the Mark
At Thinkwell, we are often asked our thoughts on polling in cases where – collectively – the polls are viewed as ‘missing the mark’ in an election campaign. The most recent example is in Newfoundland and Labrador, where the October 14 election produced a result that took many by surprise.
The province’s electorate delivered a majority for the Progressive Conservative Party (PCs), led by Tony Wakeham, with a popular vote share of approximately 44% and 21 seats in the 40-seat legislature. Meanwhile, the incumbent Liberal Party — widely expected to hold on or come out stronger in pre-election commentary — underperformed. The gap between expectation and outcome elicits important insights about political polling in general, and in Newfoundland and Labrador in particular.
In our view, there were four key areas of weakness: (1) insufficient polling frequency and sample size, (2) mischaracterization of turnout and “likely voter” models, (3) regional or respondent-bias issues unique to Newfoundland & Labrador, and (4) the impact of late-breaking events and campaign dynamics.
1. Too few irons in the fire
One of the stark limitations was the scarcity of publicly-available polls for this election. And most of those publicly released polls predicted a win for the Liberals.
A lack of polling (which is typically rare) can present several risks:
With few waves of polling leading up to the election, it is difficult to track movement — late momentum or shifts in voter preference may go unseen.
If polls show one narrative (e.g., Liberal dominance) that narrative can feed media/expectation bias, reducing the inclination to investigate alternative outcomes.
For a province like Newfoundland and Labrador, with 40 seats and significant regional variation (rural vs. Labrador vs. St. John’s area), under-sampling in certain areas may lead to blind spots. Polls may lean heavily on voters who are more accessible (urban, telephone, online research panels, older demographics) while missing more remote or non-traditional voters.
Key Takeaway: Frequent, geographically stratified polling is especially crucial in jurisdictions with dispersed populations and potentially non-standard turnout patterns. As we saw in the election, the views of urban and rural voters can vary dramatically, which naturally translates into different voting patterns.
2. Likely-voter models & turnout assumptions
Polling is only as good as its inputs – and one input that often causes systematic error is the likely-voter (LV) model. In this election, the turnout may have diverged from polling assumptions in ways that disadvantaged certain parties.
The overall voter turnout was just over 50%. Like many jurisdictions, there has been a notable decline in voter turnout over the past ten years.
If fewer people vote, or if certain demographics (i.e. younger residents) were less likely to turn out, polling assumptions about ‘likely voters’ become more fragile and can impact weighting systems used to adjust the survey samples.
Depending on the data collection method used, online panel respondents may differ meaningfully from actual voters in responsiveness and voter motivation.
Differential “enthusiasm gaps” – where one party’s supporters turn out at a higher rate than anticipated – may not show up in standard models if they rely on historical turnout or general assumptions. Or one party does a better job of ‘getting the vote out’.
Late shifts in attitude or ‘surprise’ voter mobilization are hard to capture unless polls are refreshed near election day.
In this context, if the PC campaign succeeded in mobilizing voters (or if Liberal‐inclined voters were less motivated), the polling may have over-represented the “inactive but leaning” crowd and under-represented the actual voting base.
Key Takeaway: In smaller jurisdictions with unique turnout profiles, polling should treat LV models with extra caution and incorporate sensitivity testing (e.g., what if rural turnout rises by X %?).
3. Regional & respondent biases
Newfoundland and Labrador presents several challenges that may impact polling error:
Geographic dispersion & accessibility: Some communities are very remote and/or have limited telecommunications infrastructure. Traditional landline/telephone polling may miss segments of the electorate, as would polling via online research panels.
Cultural / non-response bias: Some voters may be less likely to respond to polls (due to distrust, remote location, language/culture factors). If non-respondents lean toward a particular party, polls will under-represent them.
Sample composition: With few polls available, the effect of how the sample is constructed (online panel vs. live telephone vs. mixed mode vs. IVR) may have a significant impact. If one methodology underrepresents groups such as rural or older voters, the result may lean toward the better-sampled demographic groups (urban, more affluent, more connected).
Historical data scarcity: With smaller jurisdictions, there may simply be fewer past data points to calibrate weighting systems designed to adjust the sample to more accurately reflect the voter population (e.g., by region, age, turnout).
Key takeaway: Polling in Newfoundland and Labrador requires a unique knowledge of the province. Methodologies employed in larger provinces may be less reliable here. Polling in less-populous provinces requires a bespoke sampling design, explicitly accounting for geography, accessibility, and likely voter motivation differences.
4. Campaign dynamics & late-breaking shifts
Finally, even the best-designed polls can be knocked off-course if late shifts in voter sentiment or campaign events are strong enough to alter behavior. Some observations from this election:
With only a small margin between parties, minor shifts (in turnout, voter migration, or distribution across districts) can flip seat outcomes more dramatically than vote share suggests.
If a poll was conducted a few weeks out from election day, but the electorate shifted in that interim period (due to a debate, issues, local candidate effect, or “hidden” under-current), the poll would fail to capture it.
In smaller jurisdictions, even local effects (candidate quality in a riding, regional issues) may amplify beyond what province-wide polling can detect — adding another layer of “error”.
Key Takeaway: For all polls, it is important to identify the margin of error, outlining the impact it can have on the results. As well, frequent waves of polling, as close to election day as possible, can help to identify shifts in voter sentiment and changes in momentum.
Conclusion
The election in Newfoundland and Labrador offers a vivid case study of how polling can go off-track even in a relatively small jurisdiction. The convergence of limited polling waves, challenging geography and demographics, likely-voter modelling issues and the impact of late momentum combined to produce a surprising result for many observers.
For research firms, the lesson is clear: when working in provinces with complex terrain, less frequent polling, dispersed population and tight races, the standard national-scale playbook needs adaptation. The margin of error isn’t just statistical — it’s operational, methodological and contextual.
By improving sample design, increasing wave frequency, refining turnout models and being transparent about uncertainties, polling firms can better align their work with the realities of provincial-level contests and minimize the gap between prediction and outcome.