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Detection of Gross Errors definitions

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  • Grubbs Test
    Statistical method for identifying a single outlier in a normally distributed dataset by comparing a calculated value to a critical table value.
  • Q Test
    Technique for detecting outliers in small datasets by comparing the ratio of the gap to the range against a critical value.
  • Outlier
    Data point that significantly deviates from the rest of the dataset, potentially indicating error or anomaly.
  • Normal Distribution
    Probability distribution where data is symmetrically distributed around the mean, forming a bell-shaped curve.
  • Standard Deviation
    Measure of data spread, indicating how much values deviate from the mean within a dataset.
  • Mean
    Average value of a dataset, calculated by summing all measurements and dividing by their count.
  • G Calculated
    Value obtained by dividing the absolute difference between a questionable value and the mean by the standard deviation.
  • G Table
    Reference chart providing critical values for the Grubbs test at various confidence levels and sample sizes.
  • Q Calculated
    Ratio of the gap between a suspected outlier and its nearest neighbor to the range of the dataset.
  • Q Table
    Table listing critical values for the Q test based on sample size and confidence level.
  • Confidence Level
    Probability that a statistical result is not due to random chance, commonly set at 90%, 95%, or 99%.
  • Range
    Difference between the largest and smallest values in a dataset, used in the Q test calculation.
  • Gap
    Absolute difference between a suspected outlier and its closest neighboring value in an ordered dataset.
  • Critical Value
    Threshold from a statistical table used to determine if a calculated value indicates an outlier.
  • Number of Measurements
    Total count of data points in a dataset, influencing the selection of critical values in outlier tests.