Is bias and variance equivalent to accuracy vs precision? I looked at some graphs of high bias and high variance, and I don't really understand how the bias vs variance tradeoff is any different from accuracy vs precision?
 A: Putting aside the use of these words in the context of classification, the word "precision" is generally taken to represent "a description of random errors, a measure of statistical variability." (Wikipedia)
Even "in the sense from science," though, there are two uses of the word "accuracy" distinguished on the Wikipedia page:
a common use of the term and one adopted by the ISO. In the common use, "accuracy" is

a description of systematic errors, a measure of statistical bias...  ISO calls this trueness.

Under the ISO usage, accuracy is:

a combination of both types of observational error above (random and systematic), so high accuracy requires both high precision and high trueness.

In that latter ISO usage, measures like mean-squared or mean absolute error could represent "accuracy."
The bias-variance tradeoff in statistical modeling is often presented in terms of the expected error when applying a model to a new test set. Quoting from ISLR, pages 33 to 34, where $\hat f(x_0)$ represents the model's prediction for an input value of $x_0$:

... the expected test MSE, for a given value $x_0$, can always be decomposed into the sum of three fundamental quantities: the variance of $\hat f(x_0)$, the squared bias of $\hat f(x_0)$ and the variance of the error terms $\epsilon$. That is,
$$ E\left( y_0 − \hat f(x_0)\right)^2 = \text{Var}\left( \hat f(x_0) \right) + \left[ \text{Bias} \left( \hat f(x_0) \right) \right]^2 + \text{Var}(\epsilon)$$
Here the notation $E\left( y_0 − \hat f(x_0)\right)^2$ defines the expected test MSE, and refers
to the average test MSE that we would obtain if we repeatedly estimated
$f$ using a large number of training sets, and tested each at $x_0$. The overall
expected test MSE can be computed by averaging $E\left( y_0 − \hat f(x_0)\right)$ over all possible values of $x_0$ in the test set.

I see "bias" and "variance" in this modeling context as somewhat different from the situation where one is describing the "accuracy" and "precision" of a scientific measuring instrument. One might roughly consider: common "accuracy" or ISO "trueness" related to "bias" in modeling, "variance" related to the inherent "precision" of the instrument, and $\text{Var}(\epsilon)$ related to inherent variability in what you're trying to measure.
Yet the nature of bias and variance in modeling differs from that in evaluating a scientific measuring instrument. In the process of modeling there is a tradeoff that needs to be made between bias and variance to improve performance on a new data set. In setting up a model, one can introduce bias in a way that decreases variance enough to lower the overall mean squared error (a measure of "accuracy" in the ISO sense). The bias-variance tradeoff is key. That's not typically the situation with designing or using a scientific measuring device.
