Quotes About Inference

In his Treatise on Human Nature, the Scots philosopher David Hume posed the issue in the following way (as rephrased in the now famous black swan problem by John Stuart Mill): No amount of observations of white swans can allow the inference that all swans are white, but the observation of a single black swan is sufficient to refute that conclusion.

Almost everything in social life is produced by rare but consequential shocks and jumps; all the while almost everything studied about social life focuses on the "normal," particularly with "bell curve" methods of inference that tell you close to nothing. Why? Because the bell curve ignores large deviations, cannot handle them, yet makes us confident that we have tamed uncertainty. Its nickname in this book is GIF, Great Intellectual Fraud.

Almost everything in social life is produced by rare but consequential shocks and jumps; all the while almost everything studied about social life focuses on the normal, particularly with bell curve methods of inference that tell you close to nothing. Why? Because the bell curve ignores large deviations, cannot handle them, yet makes us confident that we have tamed uncertainty. Its nickname in this book is GIF, Great Intellectual Fraud.

How can we logically go from specific instances to reach general conclusions? How do we know what we know? How do we know that what we have observed from given objects and events suffices to enable us to figure out their other properties? There

how we tend to generalize from what we see.

Reverse-engineering problem: It is easier to predict how an ice cube would melt into a puddle than, looking at a puddle, to guess the shape of the ice cube that may have caused it. This "inverse problem" makes narrative disciplines and accounts (such as histories) suspicious.

if you seek certainty about whether the patient has cancer, not certainty about whether he is healthy, then you might be satisfied with negative inference, since it will supply you the certainty you seek.

When your sample is large, no single instance will significantly change the aggregate or the total.

Cygnus Atratus In his Treatise on Human Nature, the Scots philosopher David Hume posed the issue in the following way (as rephrased in the now famous black swan problem by John Stuart Mill): No amount of observations of white swans can allow the inference that all swans are white, but the observation of a single black swan is sufficient to refute that conclusion.

Our inferential machinery, that which we use in daily life, is not made for a complicated environment in which a statement changes markedly when its wording is slightly modified.

Statistical inference is really just the marriage of two concepts that we've already discussed: data and probability (with a little help from the central limit theorem).

This distinction between correlation and causation is crucial to the proper interpretation of statistical results.

The challenge with any "before and after" kind of analysis is that just because one thing follows another does not mean that there is a causal relationship between the two.

First of all, our best guess for what the mean of any sample will be is the mean of the population from which it's drawn.

The whole point of a representative sample is that it looks like the underlying population.

Specifically, the sample means will form a normal distribution around the population mean, which in this case is $70,900.

Life gets a little trickier when we are doing our regression analysis (or other forms of statistical inference) with a small sample of data.

Logic suggests that we should be less confident about generalizing our results to the entire adult population from a sample of 25 than from a sample of 3,000.

Instead, we have to assume that repeated samples of just 25 will produce more dispersion around the true population coefficient—and therefore a distribution with "fatter tails.

Statistics cannot prove anything with certainty. Instead, the power of statistical inference derives from observing some pattern or outcome and then using probability to determine the most likely explanation for that outcome.

For large samples, we can assume that the standard deviation of the sample is reasonably close to the standard deviation of the population.*

Motive may be inferred only when the stimuli in the agent's environment are open to view and the influences leading an agent to a particular action can be evaluated.

She knew that when somebody told you these sorts of things, they were pebbles cast into the pool and you ought to look carefully at the ripples of implication.

The heart of metaphor is inference. Conceptual metaphor allows inferences in sensory-motor domains (e.g., domains of space and objects) to be used to draw inferences about other domains (e.g., domains of subjective judgment, with concepts like intimacy, emotions, justice, and so on). Because we reason in terms of metaphor, the metaphors we use determine a great deal about how we live our lives.