What does the p value mean in meta analysis results output? What does the p value mean in a meta analysis results output? My meta analysis is looking at the proportion of people affected by PTSD. I am using the software OpenMeta Analyst which runs r in the background. 
 A: I think that quoting verbatim the Cochrane Handbook should provide you enough details:

A P value is the probability of obtaining the observed effect (or
  larger) under a ‘null hypothesis’, which in the context of Cochrane
  reviews is either an assumption of ‘no effect of the intervention’ or
  ‘no differences in the effect of intervention between studies’ (no
  heterogeneity). Thus, a P value that is very small indicates that the
  observed effect is very unlikely to have arisen purely by chance, and
  therefore provides evidence against the null hypothesis.  It has been
  common practice to interpret a P value by examining whether it is
  smaller than particular threshold values. In particular, P values less
  than 0.05 are often reported as “statistically significant”, and
  interpreted as being small enough to justify rejection of the null
  hypothesis. However, the 0.05 threshold is an arbitrary one that
  became commonly used in medical and psychological research largely
  because P values were determined by comparing the test statistic
  against tabulations of specific percentage points of statistical
  distributions. RevMan, like other statistical packages, reports
  precise P values. If review authors decide to present a P value with
  the results of a meta-analysis, they should report a precise P value,
  together with the 95% confidence interval.
In RevMan, two P values are provided. One relates to the summary
  effect in a meta-analysis and is from a Z test of the null hypothesis
  that there is no effect (or no effect on average in a random-effects
  meta-analysis). The other relates to heterogeneity between studies and
  is from a chi-squared test of the null hypothesis that there is no
  heterogeneity (see Chapter 9, Section 9.5.2).
For tests of a summary effect, the computation of P involves both
  the effect estimate and the sample size (or, more strictly, the
  precision of the effect estimate). As sample size increases, the range
  of plausible effects that could occur by chance is reduced.
  Correspondingly, the statistical significance of an effect of a
  particular magnitude will be greater (the P value will be smaller) in
  a larger study than in a smaller study.
P values are commonly misinterpreted in two ways.  First, a moderate
  or large P value (e.g. greater than 0.05) may be misinterpreted as
  evidence that “the intervention has no effect”. There is an important
  difference between this statement and the correct interpretation that
  “there is not strong evidence that the intervention has an effect”. To
  avoid such a misinterpretation, review authors should always examine
  the effect estimate and its 95% confidence interval, together with the
  P value. In small studies or small meta-analyses it is common for the
  range of effects contained in the confidence interval to include both
  no intervention effect and a substantial effect. Review authors are
  advised not to describe results as ‘not statistically significant’ or
  ‘non-significant’.
The second misinterpretation is to assume that a result with a small P
  value for the summary effect estimate implies that an intervention has
  an important benefit.  Such a misinterpretation is more likely to
  occur in large studies, such as meta-analyses that accumulate data
  over dozens of studies and thousands of participants. The P value
  addresses the question of whether the intervention effect is precisely
  nil; it does not examine whether the effect is of a magnitude of
  importance to potential recipients of the intervention. In a large
  study, a small P value may represent the detection of a trivial
  effect. Again, inspection of the point estimate and confidence
  interval helps correct interpretations (see Section 12.4.1).

More personally, I interpret the p value for effect as an indication of how much likely differences in effects in the groups of interest were simply due to random variability (null hypothesis of no difference). Accordingly, if the p value is sufficiently small (eg <0.05 or better <0.01 or <0.001), then it is unlikely that the comparators are actually similar.
