Which of the following is an implication of high variance in a statistical model?

High variance in a statistical model typically indicates that the model is too complex relative to the amount of training data available. This complexity allows the model to capture noise in the training data rather than the underlying distribution. As a result, the model fits the training data very closely but performs poorly on unseen data, which is a classic case of overfitting.

Overfitting happens when a model learns not just the underlying patterns but also the random fluctuations and noise within the training dataset. This results in a model that has low bias (as it can fit the training data very well) but high variance, leading to poor generalization to new, unseen data. The high sensitivity to the specific data points used for training means that even small changes in the input data can lead to significant changes in the model's predictions.

The other implications suggest outcomes that do not directly correlate with high variance. For instance, lower predictive performance specifically on training data would suggest an underfit model, while bias from overly simplified models is associated with low complexity rather than high variance. Lastly, claiming that high variance has no influence on prediction accuracy ignores the fundamental issues of overfitting that arise with high-variance models. Thus, the identification of overfitting as resulting from high variance is