Sample Size
Sample size is how many swings or shots a metric is based on. This means 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. This means a retest delta based on three shots may be real change or random variation. One based on twenty shots is much more trustworthy.
If you only have time for 5 swings, record them. This means some data is better than none. This means but treat the averages as directional estimates rather than confirmed baselines. Record more before making a significant technique change based on those numbers.
For retest sessions, match your sample size to the baseline session size. This means if the baseline was 15 swings, the retest should also be at least 15. Unequal sample sizes produce statistically unreliable delta interpretations.
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".
How it shows up on video
When recording for analysis, film 10 or more consecutive swings rather than 3-5. This means the skeleton overlay across a 10-rep session shows the full range of your movement pattern, including natural variation that a 3-rep session hides behind lucky consistency.
Common mistakes
- Using 3-swing sessions for baseline establishment — too small a sample to distinguish your average mechanics from natural day-to-day variation.
- Treating a single spectacular swing as representative when the sample size was small — one great swing in three is not evidence of a trained pattern.
- Not matching sample sizes between baseline and retest sessions. This means a 3-swing baseline and a 15-swing retest produce an unreliable delta. This is because the standard errors are very different.
- Ignoring sample size warnings in the confidence indicator — if the app flags low sample, do not act on that session's averages for technique decisions.
In SwingVantage Motion Lab
In Motion Lab, the confidence indicator on any session-average metric turns green at 10+ swings, yellow at 5-9, and red below 5. Use this visual guide to decide whether to trust a session average or record more reps before making a significant technique change.
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. This means low variance produces a high score. This is. This is 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. This means it may represent a genuine extreme swing or a measurement artifact. It requires honest handling before being used in averages.
Put this into your swing
SwingVantage can spot this in your own swing — free to start.