Statistical rigor,
zero complexity
Upload your data and Sledder handles the rest: model selection, outlier detection, SRM checks, and corrections. Choose Bayesian or Frequentist. Get a clear recommendation in seconds, not hours.
Why Choose Sledder
for Analysis?
Sledder picks the right statistical model, applies the necessary corrections, and delivers a clear verdict. Not a p-value for your team to debate. And the more tests you run, the sharper your Bayesian priors become.
Automatic model selection
Beta-Binomial, Normal-Inverse-Gamma, or Mann-Whitney. Sledder picks the engine that fits your metric type and data shape.
Empirical Bayes priors
Priors are fitted from your organization's past experiments and sharpen every new analysis automatically.
Actionable verdicts
Clear recommendations backed by P(B>A), Expected Loss, and 95% HDI. Readable by your whole team, not just analysts.
Dual statistical engine
Run Bayesian and Frequentist analysis side by side on the same data for independent confidence from two perspectives.
Adaptive priors
Empirical Bayes priors are fitted from your test history and tighten automatically as you accumulate results.
Automated corrections
SRM checks, outlier detection, and multiple comparison corrections are applied before you ever see the results.
Segment deep-dives
Break down results by device, geography, traffic source, or any custom segment, each with its own statistical rigor.
Sequential monitoring
Track your test as data arrives with optional stopping boundaries. No need to wait for a fixed sample size.
Decision framework
Get a clear Deploy, Iterate, or Keep Control recommendation backed by your configured risk thresholds.
Your experimentation workflow, reimagined.
Join the teams who ship winning experiments with Sledder.
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