I ran both a linear regression and Poisson regression on count data (data ranges from 0-54) with two continuous predictors and the p values were very different between them.
m <- glm(Count ~ Age + Days, data = dat, family = 'poisson') m <- lm(Count ~ Age + Days, data = dat)
Am I doing something wrong here with one of those models? The p values was significant for age and days (around .02) in the Poisson regression and non-significant for both age and days (around .5) in the linear regression. The standard errors were also lower in the Poisson regression.