  The established language for statistical testing --- significance levels,
power, and p-values --- is overly complicated and deceptively conclusive. Even
teachers of statistics and scientists who use statistics misinterpret the
results of statistical tests, tending to misstate their meaning and exaggerate
their certainty. We can communicate the meaning and limitations of statistical
evidence more clearly using the language of betting.
  This paper calls attention to a simple betting interpretation of likelihood
ratios. This interpretation leads to methods that lend themselves to
meta-analysis and accounting for multiple testing. It is closely related to the
interpretation of probability as frequency, but it does not encourage the
fallacy that probabilistic models imply the existence of unseen alternative
worlds.
  For more on the betting interpretation of probability, see
\cite{Shafer/Vovk:2019} and the other working papers at
www.probabilityandfinance.com.
