P<0.05 means the chance of this result being a statistical fluke is less than 0.05, or 1 in 20. It’s the most common standard for being considered relevant, but you’ll also see p<0.01 or smaller numbers if the data shows that the likelihood of the results being from chance are smaller than 1 in 20, like 1 in 100. The smaller the p value the better but it means you need larger data sets which costs more money out of your experiment budget to recruit subjects, buy equipment, and pay salaries. Gotta make those grant budgets stretch so researchers will go with 1 in 20 to save money since it’s the common standard.
P<0.05 means the chance of this result being a statistical fluke is less than 0.05, or 1 in 20. It’s the most common standard for being considered relevant, but you’ll also see p<0.01 or smaller numbers if the data shows that the likelihood of the results being from chance are smaller than 1 in 20, like 1 in 100. The smaller the p value the better but it means you need larger data sets which costs more money out of your experiment budget to recruit subjects, buy equipment, and pay salaries. Gotta make those grant budgets stretch so researchers will go with 1 in 20 to save money since it’s the common standard.