Why More Samples Improve Accuracy
More measurements do not make R² go up. With few points R² often comes out higher by chance and falls as data is added — that is not the model getting worse, it is the lucky fit going away.
What more data actually buys is precision. The confidence interval around each coefficient narrows roughly with the square root of the number of points: four times the data, about half the width. Predictions also stop jumping when a few rows are added or removed, and more of the operating range is covered — both ends, rare conditions, drift over time.
How many is enough? It depends on how many variables the model has, not on one fixed number. SenSight recommends at least 10 × (number of variables + 1) rows — 20 for a one-variable model — and shows how many more are needed when a model is below that. More is needed when the noise is large or the effect you want to detect is small.
Judge "enough" from the coefficient's interval, not from R²: if the interval is still too wide for the decision you have to make, collect more.
What more data actually buys is precision. The confidence interval around each coefficient narrows roughly with the square root of the number of points: four times the data, about half the width. Predictions also stop jumping when a few rows are added or removed, and more of the operating range is covered — both ends, rare conditions, drift over time.
How many is enough? It depends on how many variables the model has, not on one fixed number. SenSight recommends at least 10 × (number of variables + 1) rows — 20 for a one-variable model — and shows how many more are needed when a model is below that. More is needed when the noise is large or the effect you want to detect is small.
Judge "enough" from the coefficient's interval, not from R²: if the interval is still too wide for the decision you have to make, collect more.
Try it in the app
Try: Run a regression and look at the Std. Error column of the coefficients table. Add more rows and run it again — R² may go up or down, but the standard error shrinks. That shrinking is what more data buys.