  Attacks on the P-value are nothing new, but the recent attacks are
increasingly more serious. They come from more mainstream sources, with
widening targets such as a call to retire the significance testing altogether.
While well meaning, I believe these attacks are nevertheless misdirected:
Blaming the P-value for the naturally tentative trial-and-error process of
scientific discoveries, and presuming that banning the P-value would make the
process cleaner and less error-prone. However tentative, the skeptical
scientists still have to form unambiguous opinions, proximately to move forward
in their investigations and ultimately to present results to the wider
community. With obvious reasons, they constantly need to balance between the
false-positive and false-negative errors. How would banning the P-value or
significance tests help in this balancing act? It seems trite to say that this
balance will always depend on the relative costs or the trade-off between the
errors. These costs are highly context specific, varying by area of
applications or by stage of investigation. A calibrated but tunable knob, such
as that given by the P-value, is needed for controlling this balance. This
paper presents detailed arguments in support of the P-value.
