This kind of situation is dealt with in Quine’s Two Dogmas of Empiricism, especially the last section, “Empiricism Without the Dogmas.” This is a short (~10k words), straightforward, and influential work in the philosophy of science, so it is really worth reading the original.
Quine describes science as a network of beliefs about the world. Experimental measurements form a kind of “boundary conditions” for the beliefs. Since belief space is larger than the space of experiments which have been performed, the boundary conditions meaningfully constrain but do not fully determine the network.
The totality of our so-called knowledge or beliefs, from the most casual matters of geography and history to the profoundest laws of atomic physics or even of pure mathematics and logic, is a man-made fabric which impinges on experience only along the edges. Or, to change the figure, total science is like a field of force whose boundary conditions are experience. A conflict with experience at the periphery occasions readjustments in the interior of the field.
Some beliefs are closer to the core of the network: changing them would require changing lots of other beliefs. Some beliefs are closer to the periphery: changing them would change your beliefs about a few contingent facts about the world, but not much else.
In this example, the belief in Newton’s laws are much closer to the core than the belief in the stability of this particular pendulum.[1]
When an experiment disagrees with our expectations, it is not obvious where the change should be made. It could be made close to the edges, or it could imply that something is wrong with the core. It is often reasonable for science (as a social institution) to prefer changes made in the periphery over changes made in the core. But this is not always the implication the experiment makes.
A particular example that I am fond of involves the perihelion drifts of Uranus and Mercury. By the early 1800s, there was good evidence that the orbits of both planets were different from what Newtonian mechanics predicted. Both problems would be resolved by the mid 1900s, but the resolutions were very different. The unexpected perihelion drift of Uranus was explained by the existence of another planet in our solar system: Neptune. The number of planets in our solar system is a periphery belief: changing it does not require many other beliefs to change. People then expected that Mercury’s unexpected perihelion drift would have a similar cause: a yet undiscovered planet close to the sun, which they named Vulcan. This was wrong.[2] Instead, the explanation was the Newtonian mechanics was wrong and had to be replaced by general relativity. Even though the evidence in both cases was the same, they implied that there should be changes made at different places in the web of beliefs.
Also, figuring things out in hindsight is totally allowed in science. Many of our best predictions are actually postdictions. Predictions are more impressive, but postdictions are evidence too.
The biggest problem these students have is being too committed to not using hindsight.
I would say that this planet was not discovered, except apparently in 1859 a French physician / amateur astronomer named Lescarbault observed a black dot transiting the sun which looked like a planet with an orbital period of 19 days.
I would say that this observation was not replicated, except it was. Including by professional astronomers (Watson & Swift) who had previously discovered multiple asteroids and comets. It was not consistently replicated, and photographs of solar eclipses in 1901, 1905, and 1908 did not show it.
What should we make of these observations?
There’s always recourse to extremely small changes right next to the empirical boundary conditions. Maybe Lescarbault, Watson, Swift, & others were mistaken about what they saw. Or maybe they were lying. Or maybe you shouldn’t even believe my claim that they said this.
These sorts of dismissals might feel nasty, but they are an integral part of science. Some experiments are just wrong. Maybe you figure out why (this particular piece of equipment wasn’t working right), and maybe you don’t. Figuring out what evidence should be dismissed, what evidence requires significant but not surprising changes, and what evidence requires you to completely overhaul your belief system is a major challenge in science. Empiricism itself does not solve the problem because, as Quine points out, the web of beliefs is underdetermined by the totality of measured data.
This kind of situation is dealt with in Quine’s Two Dogmas of Empiricism, especially the last section, “Empiricism Without the Dogmas.” This is a short (~10k words), straightforward, and influential work in the philosophy of science, so it is really worth reading the original.
Quine describes science as a network of beliefs about the world. Experimental measurements form a kind of “boundary conditions” for the beliefs. Since belief space is larger than the space of experiments which have been performed, the boundary conditions meaningfully constrain but do not fully determine the network.
Some beliefs are closer to the core of the network: changing them would require changing lots of other beliefs. Some beliefs are closer to the periphery: changing them would change your beliefs about a few contingent facts about the world, but not much else.
In this example, the belief in Newton’s laws are much closer to the core than the belief in the stability of this particular pendulum.[1]
When an experiment disagrees with our expectations, it is not obvious where the change should be made. It could be made close to the edges, or it could imply that something is wrong with the core. It is often reasonable for science (as a social institution) to prefer changes made in the periphery over changes made in the core. But this is not always the implication the experiment makes.
A particular example that I am fond of involves the perihelion drifts of Uranus and Mercury. By the early 1800s, there was good evidence that the orbits of both planets were different from what Newtonian mechanics predicted. Both problems would be resolved by the mid 1900s, but the resolutions were very different. The unexpected perihelion drift of Uranus was explained by the existence of another planet in our solar system: Neptune. The number of planets in our solar system is a periphery belief: changing it does not require many other beliefs to change. People then expected that Mercury’s unexpected perihelion drift would have a similar cause: a yet undiscovered planet close to the sun, which they named Vulcan. This was wrong.[2] Instead, the explanation was the Newtonian mechanics was wrong and had to be replaced by general relativity. Even though the evidence in both cases was the same, they implied that there should be changes made at different places in the web of beliefs.
Also, figuring things out in hindsight is totally allowed in science. Many of our best predictions are actually postdictions. Predictions are more impressive, but postdictions are evidence too.
The biggest problem these students have is being too committed to not using hindsight.
I would say that this planet was not discovered, except apparently in 1859 a French physician / amateur astronomer named Lescarbault observed a black dot transiting the sun which looked like a planet with an orbital period of 19 days.
I would say that this observation was not replicated, except it was. Including by professional astronomers (Watson & Swift) who had previously discovered multiple asteroids and comets. It was not consistently replicated, and photographs of solar eclipses in 1901, 1905, and 1908 did not show it.
What should we make of these observations?
There’s always recourse to extremely small changes right next to the empirical boundary conditions. Maybe Lescarbault, Watson, Swift, & others were mistaken about what they saw. Or maybe they were lying. Or maybe you shouldn’t even believe my claim that they said this.
These sorts of dismissals might feel nasty, but they are an integral part of science. Some experiments are just wrong. Maybe you figure out why (this particular piece of equipment wasn’t working right), and maybe you don’t. Figuring out what evidence should be dismissed, what evidence requires significant but not surprising changes, and what evidence requires you to completely overhaul your belief system is a major challenge in science. Empiricism itself does not solve the problem because, as Quine points out, the web of beliefs is underdetermined by the totality of measured data.