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Effective Data Plot Assessment for Outlier Detection in Research

Do you struggle with deciding when to discard a plot in your research? Follow these steps: Observe obvious poor standards, note and examine plots, use Dixon’s Test for outliers by subtracting suspect values with the next highest or lowest and dividing by suspect minus lowest or highest. Remember, the difference must be at least 2 times. If you have to adjust 30% of plots, discard the entire test. High CV, low yields, and error variance indicate when to throw out a test. Historical error variances and comparison with pooled variances are crucial. Discarding low yielding trials may not improve predictability. Reporting means across locations is better than reporting individual data. Remember, bad data is worse than no data.

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Effective Data Plot Assessment for Outlier Detection in Research

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  1. THROWING OUT PLOTS HOW DO YOU KNOW WHEN TO THROW OUT A PLOT?

  2. MY APPROACH OBVIOUS POOR STANDS WHEN UNSURE THEN NOTE PLOTS EXAMINE DATA AFTER HARVEST USE DIXON’S TEST FOR OUTLIERS

  3. DIXON’S TEST FOR OUTLIERS SUBTRACT SUSPECT VALUE WITH NEXT HIGHEST OR LOWEST AND DIVIDE BY SUSPECT MINUS LOWEST OR HIGHEST. GENERAL RULE OF THUMB- DIFFERENCE MUST BE 2 TIMES

  4. EXAMPLE OF USING DIXON’S TEST FOR OUTLIERS PLOT VALUES ARE 20 17 22 23 41 41 – 23 / 41 – 17 = .75 PROB. < 0.05 REF. W.J. DIXON, BIOMETRICS 9:89

  5. ANOTHER EXAMPLE PLOT VALUES ARE 20 7 22 23 19 19-7 / 23-7= 0.75 PROB. < 0.05

  6. HOW MANY BAD PLOTS CONSTITUTE A BAD TEST? MY RULE – IF YOU HAVE TO ADJUST 30% OF THE PLOTS OR MORE THEN THROW OUT THE ENTIRE TEST OR AT LEAST THAT REP

  7. IF YOU THROW OUT A PLOT THEN HOW DO YOU HANDLE IT? CALCULATE A MISSING PLOT VALUE? NOT LIKELY. HOW DOES THIS AFFECT ANY SPATIAL ANALYSES LIKE NNA OR TREND?

  8. SO HOW DO YOU KNOW WHEN TO THROW OUT AN ENTIRE TEST?

  9. 1. WHEN THE CV IS HIGH ?2. HOW HIGH IS TOO HIGH?3. WHEN YIELDS ARE LOW ? HOW LOW?

  10. THROW OUT THE TEST WHEN THE ERROR VARIANCE IS TOO HIGH NEED HISTORICAL RECORD OF ERROR VARIANCES FOR EACH CROP COMPARE SUSPECT TRIAL WITH POOLED ERROR VARIANCE IF THE SUSPECT IS 2 TIMES THE POOLED ERROR VARIANCE THEN THROW THE BUM OUT Ref. Bowman and Rawlings. 1995. Agronomy Journal 87:147-151.

  11. WHAT ABOUT WHEN YOU HAVE LOW YIELDS ? STUDY IN NC SHOWED THAT DISCARDING LOW YIELDING TRIALS DID NOT IMPROVE PREDICTABILITY TRUE THAT IT IS MORE DIFFICULT TO SEPARATE MEANS WHEN THEY ARE LOW TRUE THAT LOW YIELDS CAUSE MORE QUESTIONS THAN ANSWERS MY ANSWER- DON’T REPORT INDIVIDUAL LOCATION DATA AND INCLUDE IN ACROSS LOCATION MEANS

  12. BAD DATA IS WORSE THAN NO DATA

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