When the model in your head mirrors the observation. That’s truth.
That conclusion is unsupported. Only the facts are truth.
When your mental model matches your observations and makes accurate predictions about expected values for observations that indicates that your model is at least close to the truth.
However if you control the observations, you have to be careful not to introduce a bias. For example you might be tempted not to make certain observations that would falsify your model. Or you might be tempted to not make observations that would require you to expand your model. But in both cases you admit your model doesn’t match reality and what good is it at that point?
Not quite. The truth is what the facts are based on.
Take the speed of light in vacuum, for example. We don’t know its true value. We have measured it repeatedly, to high precision, using various different methods. Those measurements are or facts. Based on those facts we estimate the speed of light in vacuum to be 299,792,458 m/s. We are quite confident that this value is at least very close to the truth, sure to how many measurements we made and how close they’re bunched together.
But if in the future more precise measurements suggest that it’s in fact closer to 299,792,458.135 m/s then we’ll learn that we’ve been less correct before.
Ok. Here’s a nice definition for truth.
When the model in your head mirrors the observation. That’s truth.
Given that, there’s 2 ways to truth. Control the model or control the observation.
That conclusion is unsupported. Only the facts are truth.
When your mental model matches your observations and makes accurate predictions about expected values for observations that indicates that your model is at least close to the truth.
However if you control the observations, you have to be careful not to introduce a bias. For example you might be tempted not to make certain observations that would falsify your model. Or you might be tempted to not make observations that would require you to expand your model. But in both cases you admit your model doesn’t match reality and what good is it at that point?
Only the facts are the truth. Got it.
Not quite. The truth is what the facts are based on.
Take the speed of light in vacuum, for example. We don’t know its true value. We have measured it repeatedly, to high precision, using various different methods. Those measurements are or facts. Based on those facts we estimate the speed of light in vacuum to be 299,792,458 m/s. We are quite confident that this value is at least very close to the truth, sure to how many measurements we made and how close they’re bunched together.
But if in the future more precise measurements suggest that it’s in fact closer to 299,792,458.135 m/s then we’ll learn that we’ve been less correct before.