a national manufacturer of ball bearings is experimenting with two different processes for producing precision ball bearings. it is important that the diameters be as close as possible to an industry standard. the output from each process is sampled, and the average error from the industry standard is measured in millimeters. the results are presented here. process a process b sample mean 2.0 3.0 standard deviation 1.0 0.5 sample size 12 14 the researcher is interested in determining whether there is evidence that the two processes yield different average errors. assume that process a is the first population. the population standard deviations are unknown but are assumed equal. what are the degrees of freedom? multiple choice 10 13 26 24

Two options being the same is the null hypothesis in inferential statistics. That the observed discrepancy is solely the result of chance is the null hypothesis. Calculating the probability that the null hypothesis is correct using statistical testing is achievable.

Always indicate the "null hypothesis" with an equal sign, and the alternative with a less-than, greater-than, or not equal sign. The researcher is looking for evidence that the two methods/processes produce different average errors since this alternative statement contains the not equal to symbol.

Hence, the correct pair of “null and alternative” hypotheses is given by,

[tex]Null Hypothesis(H0): UA = UB versus\\ H1: UA \neq UB; that is, the average .errors. for .two .processes .differ .significantly.[/tex]

[tex]"H0: UA = UB H1: UA \neq UB"[/tex]

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