This is sounding like a homework problem, so my answer will invoke the homework policy and provide hints.
Statistical software often marks the significance of results with different symbols to represent "levels" of significance. Statistical significance is continuous, but can be grouped into bins to simplify the distinctions among results of varying significance. Here's a common line in r
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
If you were looking for results at the significance level of .01, you'd be looking for p values marked with
**. This would include anything not marked with
***, which would be any p values below .001, and anything not marked with
., or without any mark at all.
Personally, I think this binning procedure reflects the ambivalence the scientific community feels toward the question, "Is the exact value of a 'p-value' meaningless?" Another sign of that appears in the way p values are often reported: "< .05" or "< .01", for instance. Of course, software often outputs results with many more digits than we want to report for any statistic, and we often round our results to a reasonably brief degree of precision like two or three digits.
It sounds like you've done most of your work already, and you know what result your exercise is looking for. Do you see any results in your output that could be rounded to the value you're looking for? Put another way, levels generally represent upper bounds on the bins for p values. Thus if you had a p = .0011, r wouldn't mark that with
***, it would use
**, because it's not quite low enough to reach the next level below .001.