Topic VIII. Finding Signal in Noise
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Context for this filter:
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LEARNING GOALS
- B. CONCEPT ACQUISITION
- Noise: The aspects of observations or stimuli that distract from, dilute, or get confused with signal, and are not signal (i.e., do not provide useful information about the target of interest).
- Observations/stimuli subject to confusion between signal and noise include communication, measurements, descriptions, etc.
- Signal-to-Noise Ratio: The relative strength of signal compared to the relative strength of noise in a given context. Obtaining meaningful information from the world requires distinguishing signal from noise. Therefore, human cognition (both scientific and otherwise) relies on techniques and tools to suppress noise and/or amplify signal (i.e., increase signal-to-noise ratio).
- It is possible to design filters to increase the signal-to-noise ratio, if you know where the noise is going to appear.
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LEARNING GOALS
- B. CONCEPT ACQUISITION
- People are (evolutionarily?) disposed to over-perceive signal (i.e., noise often gets misinterpreted as signal), perhaps because the cost of missing real signal (false negatives) is typically higher than the cost of mistaking noise for signal (false positives).
- People tend to see any regularity as a pattern (i.e., see more signal than there is), even when “patterns” occur by chance (i.e. are pure noise), e.g.: People underestimate the frequency of apparent patterns produced by randomness, leading to overperception of spurious signal much more frequently than people account for. (Events that are just coincidental are much more likely than most people expect.)
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LEARNING GOALS
- B. CONCEPT ACQUISITION
- Good decision-making under uncertainty involves having sufficient signal (an adequate test) and setting your threshold appropriately for the relative costs of false positives and false negatives.
- In some classification cases like pornography identification or graduate school admissions, there may not be a “truth of the matter” so there aren’t true “false” positives or “true” negatives, although a threshold must still be set.
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EXAMPLES
- Exemplary Quotes
- “It’s really hard to see the effect since there are so many other issues going on that act as noise, but there really appears to be a remarkable correlation between a young child’s ability to defer gratification and later successes in life.”
- “The problem is that nowadays we are inundated with stories about every scary crime that happens anywhere in the world, so this “noise" confuses us and we can’t see the striking “signal” that crime in our country has gone down dramatically in the past three decades.”
- "Any signal can count as noise, just like any noise can be considered as signal; it depends on what you're trying to see."
- Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
- "Psychologists can never learn anything from surveys, because people don't pay close enough attention."
LEARNING GOALS
- B. CONCEPT ACQUISITION
- Noise: The aspects of observations or stimuli that distract from, dilute, or get confused with signal, and are not signal (i.e., do not provide useful information about the target of interest).
- Noise is frequently, but not always, the result of random measurement fluctuations.
- C. CONCEPT APPLICATION
- Identify examples of “signal” and “noise,” recognizing that these examples are context-dependent.
- Roughly compare measurement techniques in terms of their resultant signal-to-noise ratios.
- Describe examples of techniques and tools to suppress noise and/or amplify signal (i.e., increase signal-to-noise ratio).
CLASS ELEMENTS
- Discussion Questions
- Think of cases where each of the following would be (a.) signal and (b.) noise:
- A dog barking
- A siren blaring
- Footsteps on the floor above you
- A baby screaming
- The number of times a baby elephant switches its tail
- The color of a giraffe's tongue
- The shape of the clouds
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LEARNING GOALS
- A. ATTITUDES
- Be wary of our tendency to see patterns that do not exist (to see signal where there is in fact only noise).
- C. CONCEPT APPLICATION
- Recognize and explain the flaw in a scenario where scientists mistake noise for signal.