Lecture 3 Articles.docx

# Related to whether we say 1 variable is causing

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– related to whether we say 1 variable is causing changes in the other, versus other variables that may be related to the 2 variables o Numerical analysis comprises only a small part of a statistical investigation Important first step – think of ways to organize and examine the data o Fundamental principle of statistics is that data vary There are 2 fundamental aspects of statistical thinking o (1) Data vary – values of a variable vary o (2) Analyzing pattern of variation (distribution) reveals insights Example 1 – study of whether cancer pamphlets are written at appropriate level to be read & understood o Test of reading ability given to 63 patients, readability level determined for sample of 30 pamphlets o Addressing the research question requires comparing the 2 distributions in the study Only considering median ignores variability & overall distribution of the data o Many patients had a reading level below that of the most readable pamphlet There is still uncertainty in various aspects of data even if patterns are found o (1) Measurement errors (2) Only a “snapshot” of observations Example 2 – study of whether pre-verbal infants take into account an individual’s actions towards others in evaluating that individual as appealing or aversive o Infants showed a “climber” character that could not make it up a hill in 2 tries Showed 2 scenarios – one where climber was pushed to the top be a “helper” & one where climber was pushed back down by a “hinderer” Most infants chose to play with the helper toy o Important to control for as many variables as might affect the responses Researchers controlled for color, right & left-handed tendencies, and shapes of the toys o One consideration that can’t be controlled – randomness inherent in the selection Same infants would most likely not make the same choices o P-value was 0.0021 – study provides strong evidence that there was genuine preference for helper toy Probability model – investigate results that would occur in long run if random chance were the only factor o P-value tells you how often a random process gives a result as extreme as found in the actual study P-value – probability of observing a particular outcome in a sample

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o Significance – something is statistically significant if it’s unlikely to arise by chance alone o If p < alpha, then we reject hypothesis that only random chance was at play Sample – collection of individuals on which we collect data o Population – larger collection of individuals that we would like to generalize our results to o Generalizability – whether results from sample can be generalized to a larger population
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