Monday, February 27, 2012

Deriving Meaning From Life's Outliers

An outlying observation, or outlier, is one that appears to deviate markedly from other members of the sample in which it occurs.[1]

"It is the glory of God to conceal a matter; to search out a matter is the glory of kings." - Proverbs 25:2

I never understood this verse until I saw it as a great treasure hunt.  I used to think it mean and even unfair of God to "conceal a matter" until I realised that it's actually a beautiful game of interaction:  God hides things in anticipation, knowing that a treasure hunt will ensue, and our discovery of these treasures gives Him great delight and satisfaction.  I can almost hear Him saying, "I knew you could find it!"

Perhaps, because we have gone on a hunt to discover this treasure, we appreciate and value it all the more. Hard work, awareness and commitment were involved.  Dare I say fun was involved?

Sometimes we stumble upon the treasure, sometimes it is through sheer diligence and perseverance that we find it, and maybe - in rare instances - we trip over the treasure and fall flat on our faces (bruised but laughing at the discovery). In each case, however, the treasure is cherished because it came with a price - time, hard work and tenacity.  Yet we do not receive only the treasure, when at last we find it; we also receive the delight and approval of an Almighty God who laughs with us, cries with us, and affirms us with the words, "I knew you could do it!"

What is this treasure?  It is the explanation of the outlier, the story behind the story, the discovery of meaning in relation to what previously did not make sense.

"You shall be called by a new name, which the mouth of the Lord will name." - Isaiah 62:2

... And thus is birthed the alternative story, rich with meaning.

Outliers should be investigated carefully. Often they contain valuable information about the process under investigation or the data gathering and recording process. Before considering the possible elimination of these points from the data, one should try to understand why they appeared and whether it is likely similar values will continue to appear.[2]
1. Grubbs, F. E.: 1969, Procedures for detecting outlying observations in samples. Technometrics 11, 1–21, reprinted in Wikipedia
2. Engineering Statistics Handbook,

No comments: