Despite its many benefits, analysis can be difficult to master. When it comes to the process, mistakes could lead to incorrect results that can have serious consequences. Recognizing these mistakes and avoiding them is crucial to unlock the full potential of data-driven decision making. Most of these errors result from omissions, or misinterpretations that can be easily corrected by setting clear objectives and promoting accuracy over speed.

Another common error is to assume that a variable has a normal distribution when it doesn’t. This can result in models being overor under-fitted, and virtual data rooms for real estate transactions thereby compromising confidence levels and prediction intervals. In addition, it could cause leakage between the test and the training set.

It is important to select an MA technique that is compatible with your trading style. For instance, an SMA is best suited for markets with a trend, whereas an EMA is more reactive (it removes the lag that is present in the SMA by placing priority on the most recent data). The MA parameter should be carefully chosen depending on if you are looking for an ongoing trend or a short-term one. (The 200 EMA is suitable for a longer-term timeframe).

It is essential to double-check your work prior to submitting it for review. This is especially important when working with large amounts of data as errors are more likely to occur. Having a supervisor or colleague review your work will help you spot any errors that you could have missed.