Does More Data Always Mean Better Accuracy?

– Thoughts Sparked by Dating App Advertisements

When I see dating app advertisements, numbers always come first: millions of users, billions of matches, highly advanced algorithms. The message is clear—bigger numbers must mean better results. That idea feels familiar. In statistics classes, I learned that as sample size increases, variance decreases and averages become more stable. On the surface, the logic makes sense.

But the more I paid attention to these ads, the more one question stuck with me: does having more data automatically lead to better outcomes?

The algorithms featured in these advertisements operate on the assumption that the input data is accurate. In reality, the information people enter into dating apps is rarely complete or consistent. People want to present an ideal version of themselves, leave preferences vague, or make choices that change depending on context. All of this becomes data. No matter how large the dataset grows, if its foundation is unstable, the results will be unstable as well.

That is when the phrase “garbage in, garbage out” came to mind. While the quantity of data increases, the variability and distortion within it do not disappear. In fact, larger datasets can sometimes make biases more refined and repeat them more efficiently. Increasing sample size can reduce variance, but it cannot correct flawed assumptions or poor inputs.

Since then, I no longer feel immediately persuaded by large numbers in dating app advertisements. Instead, I pause and ask questions: who is this data based on, what is missing, and what assumptions are built into the system? Big data does not necessarily provide answers—it often demands more careful questioning.

More data can lead to greater accuracy, but only under certain conditions. Realizing this shifted my attention away from the impressive numbers in advertisements and toward the realities those numbers fail to explain.