Answer:
Generalized. This is the simple example given above, with the white swans. It uses premises about a sample set to draw conclusions about a whole population.
Statistical. This form uses statistics based on a large and random sample set, and its quantifiable nature makes the conclusions stronger. For example: “95% of the swans I’ve seen on my global travels are white, therefore 95% of the world’s swans are white.”
Bayesian. This is a method of adapting statistical reasoning to take into account new or additional data. For instance, location data might allow a more precise estimate of the percentage of white swans.
Analogical. This form notes that on the basis of shared properties between two groups, they are also likely to share some further property. For example: “Swans look like geese and geese lay eggs, therefore swans also lay eggs.”
Predictive. This type of reasoning draws a conclusion about the future based on a past sample. For instance: “There have always been swans on the lake in past summers, therefore there will be swans this summer.”
Causal inference. This type of reasoning includes a causal link between the premise and the conclusion. For instance: “There have always been swans on the lake in summer, therefore the start of summer will bring swans onto the lake.”
