Consider a study whose primary results are "not statistically significant". How often does it lead to the following published conclusion that "there is no effect of the treatment/exposure on the outcome"? We believe too often and that the requirement to report counternull values could help to avoid this! In statistical parlance, the null value of an estimand is a value that is distinguished in some way from other possible values, for example a value that indicates no difference between the general health status of those treated with a new drug versus a traditional drug. A counternull value is a nonnull value of that estimand that is supported by the same amount of evidence that supports the null value. Of course, such a definition depends critically on how "evidence" is defined. Here, we consider the context of a randomized experiment where evidence is summarized by the randomization-based p-value associated with a specified sharp null hypothesis. Consequently, a counternull value has the same p-value from the randomization test as does the null value; the counternull value is rarely unique, but rather comprises a set of values. We explore advantages to reporting a counternull set in addition to the p-value associated with a null value; a first advantage is pedagogical, in that reporting it avoids the mistake of implicitly accepting a not-rejected null hypothesis; a second advantage is that the effort to construct a counternull set can be scientifically helpful by encouraging thought about nonnull values of estimands. Two examples are used to illustrate these ideas.
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