If I got values of lower .CL (confidence level) and upper .CL for a category of A factor, each o.206, 0.245 and
for b category of A factor, each 0.215 and 0.256 in R, can/may I so interpret?
: confidence Interval of a is [0.206, 0.245] and of b is [0.215, 0.256]?
- Edited
A | emmean | SE | df | lower.CL | upper.CL |
---|---|---|---|---|---|
a | 0.21 | 0.0009 | 52 | 0.206 | 0.245 |
b | 0.205 | 0.0009 | 52 | 0.215 | 0.256 |
Best Answer
To be explicit: Suppose you have the (fictitious) data sampled using R below:
A boxplot and a stripchart (dotplot) of the data are shown below:
Then (because this is simulated data) we know population mean is $\mu = 50,$ sample mean is $52.76,$ which is not significantly different from hypothetical mean $\mu_0 = 55.$ A 95% CI for $\mu$ is $(49.19, 56.33).$ which is centered at $\bar X = 52.76$ and contains $\mu_0 = 55.$
In a real application you would never know that $\mu =50,$ exactly. The best point estimate is $\hat \mu = \bar X = 52.76$ and we can be 95% confident that $\mu$ is in interval $(49.19, 56.33).$
A narrower 90% confidence interval is $(49.81, 55.71).$
To consolidate these relationships, you should look at the formula in your text or class notes for a one-sample t.test and use the summary information printed above for $n, \bar X, S_x$ to make all three confidence intervals, 90%, 95%, and 00%. Then check your hand computations with the results above from R.