True or false. determine if the following statements are true or false, and explain your reasoning. if false, state how it could be corrected. (a) if a given value (for example, the null hypothesized value of a parameter) is within a 95% confidence interval, it will also be within a 99% confidence interval. (b) decreasing the significance level (↵) will increase the probability of making a type 1 error. (c) suppose the null hypothesis is μ = 5 and we fail to reject h0. under this scenario, the true population mean is 5. (d) if the alternative hypothesis is true, then the probability of making a type 2 error and the power of a test add up to 1. (e) with large sample sizes, even small di↵erences between the null value and the true value of the parameter, a di↵erence often called the e↵ect size , will be identified as statistically significant.