Singapore’s 2025 Election and the Secret Role of Inferential Statistics

Graphic courtesy of The Straits Times

With the Singapore elections still hot on our minds, let’s take a look at how inferential statistics quietly powered one of the most-watched processes in the country.

Most people don’t associate elections with statistics — yet every sample count, margin of error, and early projection is a real-life application of concepts we often fear in school. In this article, we’ll use the 2025 General Election to show that statistics isn’t abstract or scary. It’s actually how decisions are made, predictions are formed, and — sometimes — elections are won.

🔍 The Sample Count System: How It Works

Singapore’s sample count, administered by the Elections Department (ELD), offers an early glimpse into election outcomes:

• A random sample of 100 ballots per polling station is selected and counted before the official tally.

• These results help give voters, candidates, and media a quick snapshot on polling night.

• They are generally accurate within ±4 percentage points of the final result.

This system is not about exact predictions — it’s about using a representative sample to infer the likely outcome, much like how polls, surveys, or even scientific studies work.

🥣 Understanding the Margin of Error (The Soup Analogy)

Think of inferential statistics like tasting a pot of soup:

• You taste just one spoonful to judge the whole flavor.

• Most times, that spoon reflects the whole pot.

• But sometimes:

  • You scoop more noodles than broth (sampling bias),

  • You miss spices settled at the bottom (unaccounted variation),

  • Or your spoon catches only one side of the pot (non-representative sample).

Statistical takeaway: Just like a spoonful doesn’t guarantee full accuracy, a sample count comes with uncertainty — that’s the margin of error.

📊 From Kitchen to Ballot Box: Why Results May Vary

Now let’s bring that soup metaphor back to real-world elections. Here's how sample variability can happen:

1. The "Spoonful" Effect: Pure chance may cause a sample to slightly favor one party (statistical noise).

2. The "Bottom Layer" Factor: Voters who cast ballots late in the day — like shift workers or the elderly — might skew differently than those who voted early.

3. The "Unstirred" Variable: If certain demographics are unevenly distributed across polling stations, they may be under- or over-represented in samples.

These effects are why sample counts are great for trend-spotting but imperfect for margin calls — especially in tight races.

📌 Key Battlegrounds: Where Sample Counts Mattered Most

1. East Coast GRC: The Bellwether

• Sample: 51% PAP

• Final: 52.1% PAP (+1.1%) A slim boost flipped a statistical toss-up into a confirmed win, showing the power of just a 1% swing.

2. Punggol GRC: The Shifted Mandate

• Sample: 54% PAP

• Final: 55.17% PAP (+1.17%) In this newly redrawn constituency, the sample slightly under-represented PAP’s performance.

3. Tampines GRC: The Surprise Tight Race

• Sample: 51.5% PAP

• Final: 52.8% PAP (+1.3%) Even strongholds saw competitive margins, reminding us that electoral support can shift beneath the surface.

4. Sengkang GRC: The Overestimated Lead

• Sample: 56% Opposition

• Final: 54.8% Opposition (–1.2%) Here, the sample slightly overestimated the Workers’ Party’s margin — still a win, but closer than expected.

5. Jalan Kayu SMC: The Narrow Comeback (Ng Chee Meng)

• Sample: 50.2% PAP

• Final: 51.47% PAP (+1.27%) Ng’s return came with one of the smallest winning margins, where the sample count’s modest under-call could’ve masked a critical seat win.

🧠 What We Learned About Statistics (Without the Scary Bits)

The 2025 election tells us:

• Sample counts work well — especially in contests with wide margins.

• But in close fights (under 53%), even a 1% error margin matters.

• Strong local candidates (like Ng Chee Meng) may outperform expectations due to late-deciding voters or local goodwill.

• Inferential statistics isn't magic — it’s just math helping us make smart guesses.

💬 Final Thought: Stats Are Everywhere — And They’re Not So Scary

If you’ve ever thought statistics was too dry or difficult, elections prove otherwise. Every sample count is a real-world application of confidence intervals, variability, and probability — no lab coat required.

So next time someone mentions inferential stats, you can say:“Oh, like the elections? Yeah, I get that.”