The current research provides a test framework to understand
whether and to what extent increasing public transit use and
accessibility by transit affect health. To this end, the effect of
transit mode share and accessibility by transit on general health, body
mass index, and height are investigated, while controlling for
socioeconomic, demographic, and physical activity factors. The
coefficient-p-value-sample-size chart is created and effect size
analysis are conducted to explore whether the transit use is practically
significant. Building on the results of the analysis, we found that the
transit mode share and accessibility by transit are not practically
significant, and the power of large-sample misrepresents the effect of
transit on public health. The results, also, highlight the importance of
data and variable selection by portraying a significant correlation
between transit use and height in a multivariate regression analysis.
What becomes clear from this study is that in spite of the mushrooming
interdisciplinary studies in the nexus of transportation and health
arena, researchers often propose short- and long-term policies blindly,
while failing to report the inherent explanatory power of variables. We
show that there is a thin line between false positive and true negative
results. From the weakness of p-values perspective, further, we strove
to alert both researchers and practitioners to the dangerous pitfall
deriving from the power of large- samples. Building the results on just
significance and sign of the parameter of interest is worthless, unless
the magnitude of effect size is carefully quantified post analysis.