Why is the full-sample chi square significant when the


A researcher is concerned with the relationship between attitudes toward violence and violent behavior. If attitudes "cause" behavior (a very debatable proposition), then people who have positive attitudes toward violence should have high rates of violent behavior. A pretest was conducted on 70 respondents, and among other things, the respondents were asked "Have you been involved in a violent incident of any kind over the past six months?" The researcher established the following relationship:

33_attitudes toward violence and violent behavior.png

The chi square calculated on these data is .23, which is not significant at the .05 level (confirm this conclusion with your own calculations). Undeterred by this result, the researcher proceeded with the project and gathered a random sample of 7,000. In terms of percentage distributions, the results for the full sample were exactly the same as for the pretest:

880_terms of percentage distributions.png

However, the chi square obtained is a very healthy 23.4 (confirm with your own calculations). Why is the full-sample chi square significant when the pretest was not? What happened? Do you think that the second result is important?

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Basic Statistics: Why is the full-sample chi square significant when the
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