One of my earliest experiences of studying drama included the play Gallileo by Brecht, which included the account of how sunspots were discovered by the scientist. Professor Lucie Green’s 15 Million Degrees: A Journey to the Centre of the Sun helps unpack not only these kinds of myths, but reveals a great deal of the realities of this important planetary body.

The influences of coronal mass ejections, the story of Annie Jump Cannon, recruited by Harvard in the 19th century as a computer was all news to me, and also about American physicist Robert Leighton on the magnetic field of the sun — how if it “had no magnetic field, it would be as uninteresting as most astronomers think it is.” While many of us might only consider the power of the sun when it comes to checking out eclipses, 15 Million Degrees demonstrates not only the influence on science, but the philosophy and environmental impact of the sun.

With great visual elements (including a very funny littering fine that NASA got for SKYLAB!) and the obvious passion that Professor Green has for the topic, this is a very engaging read, and one that took me a few rereads after learning just how little I knew about the subject. 15 Million Degrees: A Journey to the Centre of the Sun is a great introduction to a topic that is familiar to us all but still has such a lot for us to learn about.

Further Resources:

15 Million Degrees, A Journey to the Centre of the Sun by Lucie Green

This is the second of my book reviews of texts by presenters you can catch during the Cosmic Shambles LIVE tour.

It’s difficult to make footnotes interesting and entertaining, and the only other authors I’ve read who can do that have been Terry Pratchett and Oliver Sacks — now there’s Helen Czerski, physicist and oceanographer and the author of Storm In A Teacup — The Physics Of Everyday Life.

My first impression was that this is a book that tells us a lot about eggs (and it finally explained why rotten eggs float in water, something that I knew about but didn’t exactly know why it was a good test for an egg!). But this being physics, naturally there’s more to it than that! From fish belching (apparently they can track fish schools that way for fishing!) to different wavelengths - this is a jam-packed book that is heavy in text but light in tone. It’s a wonderfully brisk overview of some very weighty topics — including scientists and philosophers’ contributions to the field! — all done in an accessible fashion. 

While there’s no illustrations or diagrams, the engaging style of Storm In A Teacup — The Physics Of Everyday Life is a fascinating read and I enjoyed the reflection on upbringing and location having an influence on Czerski’s future career:

Further Resources:

Storm In A Teacup — Penguin Books

One of the earliest papers assigned to me for an introductory philosophy class was to take a subject and look at the early philosophical contributors to the field. Many of the students in my class took topics like art or literature — but because I had been a fairly average maths student during my high school years, I decided to challenge myself by looking at the early contributors to mathematics.

It wasn’t my highest grade in the subject, but I discovered that it wasn’t a fear or a dislike of mathematics that led me to find it boring — it was that there didn’t seem to be an atmosphere of encouraging us to enjoy the topic in school nor any sense of the historical figures and elements and exactly why they were important.

Therefore for my short series of book suggestions for the next few days are some of the books authored by the Cosmic Shambles LIVE authors, starting with Things to Make and Do in the Fourth Dimension, by Matt Parker.

This is both a complex and engaging book — complex because it covers a lot of ground in both the history of mathematical puzzles and engaging because of the style of writing. With the occasional goofy photo, this has geometrical puzzles, card tricks, how-to-mathematically-date-and-find-a-partner and even some very clever experiments conducted and documented. As the author says, ‘recreational mathematics’ can both be a lot of fun and also demonstrate that there’s a lot more to learn.

I’m hoping that my nephew doesn’t listen or read this blog, since he’s a big fan of maths and science and I’m going to be giving him this book for his birthday at the end of the month. I don’t know if he’s having a different schooling experience to me, which has led him to enjoy maths, but if this can help him enjoy it further or he can pass it onto other kids, then it’ll be a good start. I do wish that I was able to find the hidden picture of Dr Lucie Green somewhere amongst the pages before I wrapped it up and sent it off.

Karl Popper proposed the idea of falsification, that rather than starting with observations, scientists should begin with theories. And in the testing of theories, rather than trying to prove them true, the goal is to prove them false.

Any theory that is proved to be false can be modified and retested, or rejected. And in that way, science progresses by finding out whether theories are able to hold up, and can be retested to see whether or not they can be falsified. While there’s always the chance for results to continue to support a theory, if they are found to not hold up, then that calls them into question. This comes in very useful with cases of pseudoscience and irrelevant claims, as they’ll need to have a theory that can be tested.

Naturally there could be problems with using falsification, such as an error in testing of a theory or even gaps in our knowledge that make it difficult to test a theory. A proposal by Thomas Kuhn is that there’s a certain weight of evidence for certain theories and a whole new paradigm may emerge that requires a new way of interpreting evidence, or even new problems that require solving. 

While scientific developments can involve theory-based speculations leading to confirmation or retraction, there’s still the possibility for new developments and changes that could overhaul what we already know.

While inductive arguments with premises that are true (e.g swans are animals...) are easy to create, it doesn’t necessarily follow that the conclusions are going to be cogent or sound. There’s the cartoon by Randy Glasbergen that I mentioned previously for Day 16, for example:

Penguins are black and white
Some old TV shows are black and white
Therefore, some penguins are old TV shows

To recap what was previously said about arguments:

premise→ [the move is called inference]→ conclusion

When looking at arguments, we have to consider:

1) Are the reasons true?

2) Does the conclusion/s follow from those reasons — is the inference good?

If: 1 and 2 = cogent / sound. Which means we can be convinced by it.

NB: arguments are not true or false — they are only sound or unsound [as ‘un-cogent’ isn’t a word!]

Therefore:

The problem of induction is therefore about the conclusion or conclusions — premises may be right, but penguins are just not black and white TV shows.

It doesn’t necessarily mean that we can’t act in ways that assumes that there’s going to be a conclusion that holds up our initial premises — every day, we might cross the road safely, because we see there’s a green light and therefore the traffic will wait for us to cross. 

The important thing to realise is that there may be alternative explanations for what is happening — as in the case where penguins are black and white TV shows. Probability can also have an influence on results, perhaps one day there is a possibility the previously mentioned traffic light will be broken, and a green light might not make it safe to cross the road.

Next episode I’ll discuss falsification, and you can revise over arguments by looking over previous episodes on www.365daysofphilosophy.com.

For Day 78, I discussed inductive reasoning — deductive reasoning is pretty much the opposite: it begins with a particular premise and moves to a conclusion.

For example, all birds are animals — swans are birds —  therefore all swans are animals. 

In essence, it’s the taking of a theory, where you then move towards a conclusion. This conclusion, however, can be falsified, tested and investigated to find out whether or not it holds up. 

Another example is — all swans are birds — white birds are swans — all swans are white. 

Clearly this could be tested, such as are all white birds swans and whether all swans are white.

And yet, even if is found to hold up — it doesn’t necessarily mean that it will hold up forever. Black swans are a great example of a discovery that led to ‘all swans are white’ being wrong. If a theory is right now, it doesn’t necessarily mean that it can’t be falsified in the future. 

Popular culture has brought the fame of Sherlock Holmes well beyond the original stories from the 1800s, and his method of solving uses both deductive and inductive methods. There’s a great blogpost on Danielle Kinkaid’s site that outlines how this is so, and I recommend it as one of the resources for this podcast.

Inductive reasoning involves making a number of recorded observations, that can then be expanded upon with further observations to become a generalisation.

This kind of reasoning can even be used to propose what will happen with observed future — such as the sun rising every other day you’ve seen it, means you can infer that it’ll rise tomorrow.

In addition, inductive reasoning can also be used for the general to the specific and vice versa — a number of one kind of trees observed in a forest could then be used as a basis for your reasoning that there’ll be more in another part of the forrest. Inductive reasoning can involve sampling, patterns, causal relations and collection of statistics.

As previously discussed with arguments, even though one might seem convincing or have a certain flow in their structure, it doesn’t necessarily mean that it is going to be a good argument. Not all reasoning that results in a ‘some’ or ‘most’ will result in definitely or necessarily an ‘all’.

“How do we know what we know” is an interesting challenge— not all claims to knowledge have equal value. While anecdotal evidence is still a kind of evidence, it’s not the same as reproducible laboratory evidence with a hard outcome, and crystal clear methodology.

So, there’s different kinds of evidence, with the burden of proof entirely upon the claimant. What that means is if you’re making a claim that goes against the established knowledge or wisdom (whatever is accepted as being already proven, that’s already met the burden of proof) — if you’re trying to change that, you have to at least equal that. In other words, not all propositions are equal unless you can balance out with equal weight of new evidence. 

There’s many things that can influence our perceptions of events and even what we might think is good evidence — our background knowledge can influence our perceptions — including prior knowledge, expectations or cultural upbringing. The words we use can influence how we’re interpreted by others, such as something being lost in translation; and naturally, we can’t exhaustively test everything all the time. 

Sample sizes can vary and have an influence on the final results — and replication could also change in different environments — such as the boiling point of oil in space or on another planet. The important thing is that we know about these kinds of limitations and factor them in, while still working towards understanding. 

There’s a number of accessible and enjoyable books in the Very Short Introduction series and Samir Okasha’s Philosophy Of Science: A Very Short Introduction is no exception.

As you might expect, it begins with a history of modern science, including Aristotle, Copernicus and ancient scientists and mathematicians, then takes a look at Karl Popper and falsifiability, and Thomas Khun’s “The Structure of Scientific Revolutions”, along with some modern developments in the field. While there’s photos and diagrams, it’s a little light on induction and deduction, although it does reference Hume and his contributions. If you’re wondering about the basic methods of scientific reasoning and the ideas of realism and anti-realism, and logical positivism, it has great summaries of them all.

While this book is ‘short introduction’, it couldn’t cover much on paradigm shifts and how scientists, like other human beings, can have biases and a priori assumptions, it does cover as much ground as it can; I learned for the first time the debates by Newton and Leibniz on space. I know that there’s many other books that discuss the rise of intelligent design in detail, and what it means for the teaching and understanding of science, and I’d probably recommend those in comparison, but for an introduction to philosophy of science, it sets the reader on the right track. 

In using the scientific method, there’s an expectation that the scientists doing the testing should be as unprejudiced and uninfluenced as possible. That can be difficult to do, for a number of reasons!

A large amount of data is collected, based upon gathered observations, leading to the development of a theory to explain what was going on with the experiment, as discussed previously. However, not every theory becomes a universal statement or even then a law of nature. Sometimes there’s withdrawing of findings or new technological developments that lead to revised theories and experiments.

What we know about space, gravity, time has changed a lot since the early theories by the likes of Xeno in Ancient Greece who had puzzling paradoxes about speed and motion — these days we have mathematical formulae and laws and experiments that are able to solve such questions and even give us insight to what the galaxy is like.

We have to consider that scientists are humans too, that there could be discrepancies in results due to human error. One such example was the theory of cold fusion, that a type of nuclear reaction that would occur at, or near, room temperature. While there were some indications that an experiment had demonstrated that it was possible, further testing found that it couldn’t be replicated. When looking at philosophy of science, we have to consider how observations and inductive/deductive approaches influence our findings.