- $n=12$
- $\bar{x}=? $ sample average =
- $\sigma $ standard deviation =
- $\alpha =$
- $H_a :$
- Degrees of freedom if applicable
- Critical value(s) =
- Sample mean
Standard error of mean = $\frac{\sigma } {\sqrt {n}} $
$ = \dfrac{\bar{x}-\mu}{SE}$
Approximate P-value =
Decision: Reject/Accept $H_0$
Interpret this decision:
how do i gather this information ,is it sufficient to gather this information in order to approach this question ?
Actually, I don't think $n=12$; $12$ is the number of categories, and I think you're interested in the number of respondents. $ \alpha =5$% is your significance level. I assume your initial hypothesis is that there is no connection between self-image and choice of model. This means that the proportion of trait -to-model is $25$%, or $ 4$-to-$1$ . Now, you need to put this claim to a test at the $5$% significant level ( or $95$% confidence level.)
Now, you need to test whether the ratios of self-image to choice-of-model differs from the initial hypothesis "enough" ( given the choice of significance level) to be accepted or rejected at that level of significance.
As an example, you want to know if a self-described 'defensive' driver is more likely to select car A, car B, or car C , etc. Then you calculate the statistic $t$ and make a decision , looking at a t-table at the $95$% confidence level. I don't remember the details of calculating the degrees of freedom; let me look it up.