This script was developed for personal use and the idea is spotting candles that are at least 99% bigger than average (using N = 3) as they will cross the upper and lower confidence interval limits. N = 2 would roughly provide a 95% confidence interval.
I recommend the standard values N = 2 and N = 3 that provide, respectively, approximately 95% and 99% confidence intervals.
Note: I suggest using smaller sample sizes (between the 30 and 100 last candles) for sigma estimation as they tend to represent better the recent volatility. I also suggest to use sample size=400 for long-term average volatility.
Remark: the original interpretation is a bit misleading. When the series crosses over the interval limits, one can say that the current candle length is 95% or 99% as extreme as the expected length.
Also in hypothesis testing, one could say that the hypothesis of the candle length being within the expected range is rejected at 5% or 1% significance level.
As closing prices can be seen as a random walk on chart, this series is basically modelling its error.
An analogous approach for candle length is just thinking of it as changes in the closing price ( I would rename ir as Price Change Outlier Detector if TV allowed it!).
A green column means a positive change in price and a red column means a negative change in price. These changes are always relative to the last price.
If a column crosses from from the inner band to the outer band, the change in price is considered to be approximately 95% as extreme as expected.
If it crosses both bands, the change in price it considered to be approximately 99% as extreme as expected.
In true TradingView spirit, the author of this script has published it open-source, so traders can understand and verify it. Cheers to the author! You may use it for free, but reuse of this code in a publication is governed by House Rules. You can favorite it to use it on a chart.