Sample Size
Sample size is how many swings or shots a metric is based on — small samples produce more noise and lower confidence; larger samples produce more reliable estimates.
One great shot or one terrible session tells you very little. SwingVantage always shows the sample size behind any metric and adjusts confidence scores accordingly. A path average derived from three swings is noisier than one from thirty. This is especially important for retest interpretation: a retest delta based on three shots may be real change or random variation; one based on twenty shots is much more trustworthy.
Example
A -4 degree path improvement derived from 3 swings is labeled "low sample" with reduced confidence; the same delta from 20 swings is labeled "moderate confidence".
Related terms
- Signal vs NoiseSignal is the real, repeatable pattern in your swing data; noise is the random variation that looks like a pattern but isn't. Distinguishing the two is what separates useful analysis from false precision.
- Confidence ScoreA confidence score is a 0–100 calibration of how much to trust a finding, scaled by sample size, shot-to-shot consistency, and how complete your inputs were.
- Consistency ScoreA consistency score measures how tightly grouped your metrics are across multiple swings — low variance produces a high score, because consistency is often more valuable than peak performance.
- Outlier in Swing DataAn outlier is a data point that falls far outside the normal range for that metric and session — it may represent a genuine extreme swing or a measurement artifact, and it requires honest handling before being used in averages.
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