Why are measures of variability essential to inferential statistics?

They help us indicate the probability that our hypotheses are correct. If all scores feel at the exact same point, we would be absolutely sure of the values for measures of central tendency from any one sample.

However, as variability increases, our confidence intervals also increase, making us less sure, for example, that the population mean is truly indicated by the mean of a sample.

I hope this helps. Thanks for asking.