Hiya
@BobThebIlly
First off, well done on those impressive scores for assumptions (88%) and evaluating arguments (100%). Those are fantastic and show you’ve really nailed those sections! Let’s focus on the “drawing conclusions” part and see how you can improve in the short time you have.
The Watson Glaser tests your ability to draw conclusions in two specific sections - the deduction section, as well as the inference section.
Deductions: This section tests your ability to make a
deduction. With deductions, you are trying to find what follows absolutely and necessarily from the premises you are given, and just assume that all those premises are true. For example:
- Premise 1: All cats have whiskers
- Premise 2: Ram is a cat (this premise is false, but for the purpose of your deduction just assume it's true)
- Conclusion: Ram has whiskers
Notice that, in the above argument, if you assume the initial premises are true, then the conclusion follows necessarily and absolutely. This reflects the way you should be 'drawing conclusions' in the deduction section.
The inference section, by contrast, tests your ability to draw conclusions in more probabilistic ways. They are
not asking you to identify what follows absolutely or necessarily. Rather, they involve asking what conclusions are probable or strongly suggested by the evidence though not certain (e.g. follow strongly). For the purposes of the inference section, there are two styles of reasoning that you should become familiar with:
- Inductions: Imagine you’re a scientist studying bird migration. Over the course of several years, you observe that geese in a particular region always migrate south during the winter. Based on these repeated observations, you draw the conclusion "Geese in this region migrate south every winter." This is a good conclusion to draw because it's based on consistent and repeated evidence. However, it’s not certain (there could be a year when some geese don’t migrate for an unexpected reason, like illness or environmental changes). Inductive reasoning makes predictions about the future or generalisations about a group based on observed patterns. To understand whether an inference is a strong one, you'll also want to familiarise yourself with the ways people get inductions wrong. These include, but are not limited to:
- Overgeneralising: This occurs when someone draws a broad conclusion based on too few examples. For instance, seeing two aggressive dogs and concluding that all dogs are aggressive is an overgeneralisation. The sample size is too small to justify the conclusion.
- Sampling Bias: Drawing conclusions from an unrepresentative sample can lead to faulty reasoning. For example, surveying only a small group of people from one region and assuming their preferences reflect an entire population’s preferences is misleading.
- Ignoring Counterexamples: Inductive reasoning requires considering exceptions, but people sometimes disregard counterexamples that weaken their conclusions. For instance, concluding that "all swans are white" without accounting for black swans ignores evidence that challenges the generalisation. Pay attention to whether the question stem and information you're being offered provides any potential counter evidence.
- Confusing causation and correlation: People often assume that because two things happen together, one causes the other. For example, observing that ice cream sales increase in summer alongside shark attacks might lead someone to wrongly conclude that eating ice cream causes shark attacks. In reality, both are linked to a third factor: hot weather.
- Abductions: This involves selecting the most likely explanation based on the available evidence. For example, if you find fur on your couch and a chewed slipper, you might reasonably conclude that your dog is responsible. While other explanations are logically possible (e.g. such as a neighbour's cat sneaking into your house unnoticed to chew the slipper and shed fur on the couch) - these are far less plausible, especially if you have a dog at home. Abductive reasoning is particularly useful in situations where the evidence is incomplete or ambiguous. It allows us to make practical, reasonable conclusions by focusing on the explanation that best fits the facts. This approach is commonly used in problem-solving, diagnosing issues, and decision-making, as it prioritises what is most likely rather than what is merely possible.
Appreciating these different ways of 'drawing a conclusion' is important because you want to ensure that you're using the appropriate form of reasoning depending on the section you're working on. Mistaking one for another can lead to choosing the wrong answers in that section.
Hope this helps and my apologies in advance for the length of my reply!
