Bias is error that comes from a model's assumptions being too simple for reality. A straight line fitted to a curved relationship is biased. It misses the shape no matter how many rows you feed it.
Variance is how much the fitted model changes when you swap in a different training sample. A high-variance model chases the particular rows it saw. Two samples drawn from the same source give you two noticeably different models.
The dartboard picture is the fastest way in. Bias is aiming at the wrong spot, and variance is a shaky hand. You can be consistently wrong, randomly wrong, both, or neither.
Keeping the two words separate matters because they point at different repairs. Bad aim and a shaky hand are not fixed by the same change.
Rewriting in plainer words…
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