Asurprising discovery. an intern at a quantitative hedge fund examines the daily returns of around 400 stocks over one year (which has 250 trading days). she tells her supervisor that she has discovered that the returns of one of the stocks, google (goog), can be expressed as a linear combination of the others, which include many stocks that are unrelated to google (say, in a different type of business or sector). her supervisor then says: "it is overwhelmingly unlikely that a linear combination of the returns of unrelated companies can reproduce the daily return of goog. so you’ve made a mistake in your calculations." is the supervisor right? did the intern make a mistake? give a very brief explanation.

Reliability and validity reliability and validity are two important considerations that must be made with any type of data collection. reliability refers to the ability to consistently produce a given result. in the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways. unfortunately, being consistent in measurement does not necessarily mean that you have measured something correctly. to illustrate this concept, consider a kitchen scale that would be used to measure the weight of cereal that you eat in the morning. if the scale is not properly calibrated, it may consistently under- or overestimate the amount of cereal that’s being measured. while the scale is highly reliable in producing consistent results (e. g., the same amount of cereal poured onto the scale produces the same reading each time), those results are incorrect. this is where validity comes into play. validity refers to the extent to which a given instrument or tool accurately measures what it’s supposed to measure. while any valid measure is by necessity reliable, the reverse is not necessarily true. researchers strive to use instruments that are both highly reliable and valid.