I've processed my data in two ways to determine significant difference, one method suggests a very high significant differences (p = 0.0000...), while the other returns non-significant results (p = 0.52). Immediately the second option seems more likely, as having such a small p number is suspicious to me, but if someone could tell me which of the following methods is correct (or if neither are correct please let me know) it would be really appreciated.
Data has been collected from four subjects for two tasks. First task involved monitoring heart rate for 1 min before an exercise task, and another 1 min is monitoring of the heart rate following the task.
Method 1) Take the one minute data set per subject for the 'before' task, and average them, so I have one data set representing the one minute. Do the same for the 'after' task. Data is sampled at 100Hz, so there are 6000 data points per 'before' and 'after' task. Use this data for the T-test and get p = 0.0000 (very small).
Method 2) Take the average of the one minute data set per subject so I have a single value, do the same for all subjects so I have four values each representing the average heart rate of that subject for the 'before' task. Do the same for the 'after' task so I have the average heart rate per subject following the exercise. Take these much smaller data sets (4 per set) and get p = 0.52.
Are either correct, or both wrong? Any suggestions would be great.