To answer your problem you use a method, collect (and possibly analyze) data, and draw conclusions from the data. Reliability means if others will get the same result as you if they replicate your work.

Reliability problems can occur if you use the wrong method for data collection. One example is if you only take notes instead of recording an interview. The result then depends on your memory, which probably is not very reliable when it comes to remembering all details.

Another example is if you want to measure execution time for a new algorithm. If you start your timer too early you might include the time it takes to read a data file or print logging text to the screen, which introduces some error in your measurements. You shall only measure the execution time for the actual algorithm.

If you manually measure something, an error can be introduced if you are unsure about how to handle the measurement instrumentation or the reaction time when stopping a clock.

If you are doing a demanding and exhausting study on participants, the results will most likely differ depending on how hungry or tired the participants are. If you don’t count for this, you introduce error in your data.

To reduce problems with reliability you shall use conventional methods for data collection for your type of problem. What tools, methods and techniques have others working on similar problems used?