  Experimentation involves risk. The investigator expends time and money in the
pursuit of data that supports a hypothesis. In the end, the investigator may
find that all of these costs were for naught and the data fail to reject the
null. Furthermore, the investigator may not be able to test other hypotheses
with the same data set in order to avoid false positives due to p-hacking.
Therefore, there is a need for a mechanism for investigators to hedge the risk
of financial and statistical bankruptcy in the business of experimentation.
  In this work, we build on the game-theoretic statistics framework to enable
an investigator to hedge their bets against the null hypothesis and thus avoid
ruin. First, we describe a method by which the investigator's test martingale
wealth process can be capitalized by solving for the risk-neutral price. Then,
we show that a portfolio that comprises the risky test martingale and a
risk-free process is still a test martingale which enables the investigator to
select a particular risk-return position using Markowitz portfolio theory.
Finally, we show that a function that is derivative of the test martingale
process can be constructed and used as a hedging instrument by the investigator
or as a speculative instrument by a risk-seeking investor who wants to
participate in the potential returns of the uncertain experiment wealth
process. Together, these instruments enable an investigator to hedge the risk
of ruin and they enable a investigator to efficiently hedge experimental risk.
