The third argument from Van Til that we will examine is the argument from induction. Van Til argues (rightly) that we must be able to use induction in order to be able to reason about the world. That is, we have to be able to reason from our past experiences as individuals and as a society and infer the future. But to reason this way, we must assume that reality has a kind of uniformity or intelligibility. And according to Van Til, the only way we can know this is through theism.

**Does Christianity offer a solution to induction?**

The first step in evaluating Van TIl’s argument is discussing whether Christianity can actually justify induction as we use it. I am not currently aware of any serious arguments that induction is impossible under Christianity, and I think it’s reasonably clear that under Christianity we can perform induction. How do we know that reality is regular or predictable in the right kind of way? Because the God of order and knowledge created not only a world that is ordered and knowable, but also our minds. And since He created our minds intending that they would know the world, we can know the world through induction.

It’s true that some argue that under sceptical theism, we cannot do induction. We may discuss this more when we discuss solutions to the evidential problem of evil, but it doesn’t apply to theism in general.

**Secular justifications of induction**

In order for Van Til’s argument to succeed, it must not only be the case that theism allows for induction, but that there is no coherent secular response to the problem as well. Many attempts have been made at secular answers to this problem, we will have a brief look at some of them here.

**Popper: Falsification, Not Induction**

Karl Popper has famously argued that inductive reasoning ought not to be performed in the manner that is normally considered here. Instead of looking for observations to confirm or verify our hypothesis, we should instead look for observations that falsify the hypothesis. And if we don’t find any, we don’t consider the hypothesis *true*, we just consider it to be not yet falsified.

This approach is perhaps the dominant approach in philosophy of science and indeed in the practice of science. However, I think it is somewhat difficult to swallow. We end up not really believing that things are “true”, instead we believe they are “not yet proven false”. But that’s simply not how we reason about the world, we do think it is true that our various inductive hypotheses are correct. We do think it is true that the sun will rise tomorrow because we have observed it doing so in the past. So while here we do have a coherent way of reasoning, it doesn’t save our normal, everyday reasoning using induction. Therefore this is not a good enough response to the problem of induction

**Law of Large Numbers**

This is another, less popular (though I think stronger) response to the problem of induction. Helpfully explained by this Reddit comment (the whole /r/askphilosophy subreddit is pretty great by the way), we can justify induction essentially a priori using some mathematics. However, it is not without its issues as well. I will quote the SEP:

The more problematic step in the argument is the final step, which takes us from the claim that samples match their populations with high probability to the claim that having seen a particular sample frequency, the population from which the sample is drawn has frequency close to the sample frequency with high probability. The problem here is a subtle shift in what is meant by “high probability”, which has formed the basis of a common misreading of Bernouilli’s theorem. Hacking (1975: 156–59) puts the point in the following terms. Bernouilli’s theorem licenses the claim that much more often than not, a small interval around the sample frequency will include the true population frequency. In other words, it is highly probable in the sense of “usually right” to say that the sample matches its population. But this does not imply that the proposition that a small interval around the sample will contain the true population frequency is highly probable in the sense of “credible on each occasion of use”. This would mean that for any given sample, it is highly credible that the sample matches its population. It is quite compatible with the claim that it is “usually right” that the sample matches its population to say that there are some samples which do not match their populations at all. Thus one cannot conclude from Bernouilli’s theorem that for any given sample frequency, we should assign high probability to the proposition that a small interval around the sample frequency will contain the true population frequency. But this is exactly the slide that Williams makes in the final step of his argument. Maher (1996) argues in a similar fashion that the last step of the Williams-Stove argument is fallacious. In fact, if one wants to draw conclusions about the probability of the population frequency given the sample frequency, the proper way to do so is by using the Bayesian method described in the previous section. But, as we there saw, this requires the assignment of prior probabilities, and this explains why many people have thought that the combinatorial solution somehow illicitly presupposed an assumption like the principle of indifference. The Williams-Stove argument does not in fact give us an alternative way of inverting the probabilities which somehow bypasses all the issues that Bayesians have faced.

In simpler terms, it has been objected that this response to the problem of induction incorrectly assumes that the sample distribution matches the population distribution. That is, it incorrectly assumes that what we have observed is representative of some sort of universal law. Which is in fact precisely the thing that we are trying to prove. Presumably, the proponents of this solution would argue that in general, we assume that a sample is drawn randomly unless we have any reason to suspect otherwise unless we can demonstrate a bias. But that’s not necessarily true, often sampling measures come under scrutiny and must demonstrate their random methodology.

I think this solution is stronger than the previous one, however.

Perhaps in the future, we will consider more solutions to the problem of induction, but here I have presented the most common one and one that I think is quite interesting.

1. The intrinsic, objective probability of the hypothesis that nature is uniform is higher than the hypothesis that nature is not uniform. This is because, all other things being equal, the probability of simple hypotheses is always greater then the probability of more complex hypotheses, and diversity in nature is a more complex hypothesis than uniformity in nature. If nature is uniform, then there will be a description of nature striking a balance between simplicity and generality. Therefore, the intrinsic, objective probability of the hypothesis that there are laws of nature is higher than the intrinsic, objective probability that there are no laws of nature.

2. The probability of a hypothesis can be lowered by future physical inquiry — that is, even if the intrinsic probability might be high, the posterior probability might be low. Nonetheless, we know of nothing, either from observation or by way of experiment, which would suggest that we should lower the probability of the hypothesis that nature is uniform. In fact, given what we observe in nature, we should infer that the probability that there are laws of nature is very high, indeed. We are rationally entitled to utilize induction for the reasons outlined above. Given that we are rationally entitled to utilize induction, we can utilize induction to show that the probability of the hypothesis that there are laws of nature is high.