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The key to appropriately balancing exploratory and con rmatory work is to keep clear what has been hypothesized in advance and what is being discovered for the rst time Discoveries are important, but do not count as con rmations After years of extensive eldwork an entomologist develops a prediction that butter ies with a certain pattern of spotting should exist on top of a particular mountain, and sets off for the mountaintop Clearly, if the entomologist nds such butter ies there will be evidence in support of her theory; otherwise, there is an absence of evidence On her way to the mountain she traverses a jungle in which she encounters a previously unknown species of butter ies with quite unanticipated spottings.



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1934 1945: TM entered Brooklyn s P.S. 177 in a first-grade class for children who spoke only Yiddish. He was taught algebra and geometry that year at home by his paternal grandfather and invited to attend a gifted program at New York City s Hunter College in third grade. He returned to fifth grade in Brooklyn to an excellent and kindly teacher, Martin Greenspan; a special friendship developed with fellow school artist, Maurice Sendak. His junior high school English teacher, David Oberman, was highly encouraging and stimulating, as was Lafayette High School math teacher, Albert Freilich. Friendly but competitive relationships emerged with fellow students Izzy Mandelbaum and Ed Murray, the latter a future university psychology colleague. Notable throughout this period was a preoccupation with theater and imitative singing (Bing Crosby, Perry Como, Danny Kaye), suggesting a possible career direction. That, as well as a serious future as an artist, was quickly dismissed by family as an inappropriate aspiration.

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How does she handle this Should she ignore the butter y because she has not hypothesized it Should she ignore it because it may simply be a misleading Type I error Should she ignore it because she may change her original hypothesis to say she has really hypothesized this jungle butter y.

6.5 (a) Draw the trellis diagram of the binary convolutional code generated by the encoder of Figure P.6.3, and determine its minimum free distance.

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DOMINANCE ANALYSIS FOR CORRELATED DATA Paired Data d Analysis This section discusses the application of d to correlated data (e.g., repeated measures or dependent groups), as described by Cliff (1993, 1996a, 1996b). For paired observations, can be used to measure the probability that a score from one occasion is higher than a score from another occasion, just as in the independent groups context. With the dominance variable dih de ned as sign(xi xh ), where xi represents any observation on the rst occasion, xh in the second, can be de ned as E(dih ). For n pairs of observations, can be estimated by d= dih /n2 . In other words, this de nition of combines information about within-pair changes with information about between-pair changes. The calculation of d involves comparison of each of the scores on one occasion to each of the scores on the other occasion. In practice, the probability that an individual changes in a certain direction could be different from the probability that a random score from the rst occasion is higher than a random score from the second. Therefore, for paired observations, it is necessary to distinguish the within-pair (or within-subject) difference from the between-pair (or between-subject) difference. Cliff (1996b) de ned a w re ecting the probability that individuals change in a certain direction as:

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