Claims, Explanations and Inferences, Oh My!
Last time, we spoke about how critical thinking was an aspect of data science that was often over looked. In this post, we’re going to examine some of the fundamental building blocks of critical thinking: claims, explanations and inferences.
Claims
Firstly, claims. Claims are really just assertions:
- The crime rate has fallen this year.
- Unemployment is up.
- Major corporations aren’t paying enough tax.
And they come in four main flavours:
- Evidence based claims. These are claims that are stated as facts that can be checked by someone. Crime fell by 8% last year.
- Prediction base claims, which are claims that state something will happen in the future. The UK is condemned to a decade of washed out summers.
- Recommendation based claims, which are those that make recommendations. We should drink 1.2 litres of water per day.
- Principle base claims, are those that express an opinion on what ought (or not) to be done. Major corporations should pay more tax.
So what should we do when we come across these claims in our work as data scientists? Well as critical thinkers, we should always question them, asking ourselves questions like, “is this claim reasonable?”, “Is it significant?” and “What else do I need to know to make a judgement regarding this claim?”.
**Explanations
**Explanations are the things that sit between the claim and the inference. We want to get to the inference because that’s the thing that contains the action point or the conclusion to the argument, but without one or more explanations we can’t get there. Often the explanation sentence will start with “because…” or “due to…”, for example: the UK is condemned to a decade of washed out summers, due to global warming.
They often come in the form of a claim, as in this case. There are implied claims in this explanation, namely: global warming exists, global warming causes weather change and that the UK is affected by this weather change. The same questions asked of claims should be asked of these kinds of explanations too, and you should follow the claim –> explanation “rabbit hole” until it “bottoms out”, or until you satisfy yourself that the explanation is right or wrong.
Explanations can come in the form of single explanations, multiple independent explanations and joint explanations. We’ve covered single explanations; multiple independent explanations are just where more than one explanation can lead from the claim to the inference. For example: I will buy flowers because it is my wife’s birthday and because she likes flowers. Either explanation can be used to explain why flowers will be bought. Joint explanations is where two or more explanations are used, jointly, to explain a claim. However, in this case, the joint explanations are not independent and if one of the explanations are false, then the claim falls. For example: I am going to get wet when leaving work because it is going to rain and I have no umbrella. Here if it doesn’t rain, or someone lends me an umbrella, then the claim falls and I shan’t get wet.
Inference
An inference is the conclusion to an argument and often contains an action point, it follows the logical steps of claim –> explanation –> inference. Often the inference sentence will start with “So…” or “Therefore…”, for example: There is a huge demand for thingummies in the US, because legislation has been passed requiring every citizen to carry a thingummy, therefore we should increase thingummy sales to the US in the coming quarter.
So now you have been furnished with the basic steps that you should run through when you see claims made as the result of your own, or other’s, data science. If the output is in the form of a claim (sales are up in the north west region), look for explanations that can support the claim and an inference that can help the business move forward.
Next time we’ll continue our exploration of critical thinking, until then, crunch those numbers!