ready
I reread your comment and realized it sounds like you don’t know how studies are designed or interpreted.
Here is a quick crash course.
Say you want to investigate something like does candy cure cancer or does HCQ+zinc cure (or prevent) Covid19. You take two SIMILAR groups give one the treatment you are investigating (candy or HCQ) and the other standard care (this one is the control group) and you see if there is a difference in outcome between the two.
Ideally you randomize who gets what and blind the pateint and person looking for outcome, but this isnt always possible, and that is fine.
With me so far?
now you start with a null hypothesis. The null hypothesis is the default position, ie that there is no difference that candy doesnt cure cancer or that HCQ doesnt treat or prevent Covid 19.
If you find a statistically significant difference between the two groups (ie more candy recipients survived or more HCQ recipients survived or for that matter, fewer were hospitalized) You REJECT the null hypothesis, ie you reject the premise that there is no difference and accept the alternative Hypothesis that Candy DOES cure cancer or that HCQ DOES treat/prevent covid19.
Still with me?
Now how do we define what is statistically significant? Obviously it is THEORETICALLY possible that in any 2 groups of cancer patients one will do better than the other, or in any 2 groups of Covid19 patients one group will randomly do better than the other (even if they ARE similar groups) .?
So to estimate the odds that chance alone led to the difference between the two groups we calculate a p value. The p value is the probability that chance alone led to the perceived difference. A p value of < 0.05 is viewed as significant. This means that there is only a 5% chance that the perceived difference was due to chance and not due to the experimental entity. (candy or HCQ). If the p value is less than 0.05 we reject the null hypothesis (that candy doesn’t cure cancer, that HCQ doesnt cure/prevent covid19) and accept the alternative hypothesis (that it does)
Ok. got it?
Now to Dr. Zelenko’s study
the null hypothesis is that HCQ does not prevent hospitlizations, nor reduce mortality. Ie if you took two similar groups gave one HCQ and the other you dint , the two groups would have similar outcomes vis a vis death and hospitlizations.
what was the outcome?
With regard to hospitalizations they found fewer hospitilizations in the HCQ groups. Was this statisctly significant? the P value was <0.001 so yes! We reject the null hypothesis (that HCQ does not lower hospitalizations) and accept the alternative hypothesi ) HCQ DOES lower hospitalizations. *
With regard to mortality they found fewer deaths in the HCQ groups. Was this statistically significant? the P value was 0.16 so NO, Therefore We cannot reject the null hypothesis (that HCQ does not lower mortality) . (To be clear the study did NOT show that HCQ DOES NOT lower mortality, it just did NOT show that it DOES). Any attempted explanation as to WHY they didnt achieve statistical significance is mumbo jumbo trying to make their insignificant finding more meaningful.
As explained before the fact that we dont know anything about the control group doesnt make the study BETTER, the opposite it raises serious questions about their Significant finding (regarding hospitalizations)
Hope this helps
*Of course if the two groups are dissimilar for another reason aside from the presence or absence of HCQ then the difference may not be due to HCQ but due to the other differences eg younger healthier etc As I explained above )