Hypothesis Testing of Problems Missouri

Hypothesis testing of problems with one variable requires carrying out the following steps. Read on and you'll know.

Local Companies

Music/Arts Institute
(816) 836-1998
1010 S. Pearl St.
Kansas City, MO
Online Trading Academy
(913) 956-7050
7501 College Blvd., Ste. 275
Kansas City, MO
University of Missouri-Kansas City-Office of External Affairs
(816) 235-1101
5100 Rockhill Rd., AC 361
Kansas City, MO
Entrepreneurial Education Foundation/FastTrac National Headquarters
(816) 235-6575
4747 Troost Ave., Ste. 120
Kansas City, MO
Grandview C-4 School District
(816) 316-5000
13015 10th St
Grandview, MO
Genesis School, Inc.
(816) 245-5103
3800 E. 44th St
Kansas City, MO
Eldertech Solutions
314.546.4232
520 Kingsland
St. Louis, MO
Metropolitan Ensemble Theatre
(816) 516-3413
820 W. 40th St.
Kansas City, MO
P/Strada, LLC
(816) 256-4577
4033 Central
Kansas City, MO
Strider & Cline, Inc.
(816) 746-8100
7420 N. Granby Ave.
Kansas City, MO

Hypothesis testing of problems with one variable requires carrying out the following steps:

  1. State the null hypothesis and the alternative hypothesis.

  2. Decide on a significance level for the test.

  3. Compute the value of a test statistic.

  4. Compare the test statistic to a critical value from the appropriate probability distribution corresponding to your chosen level of significance and observe whether the test statistic falls within the region of acceptance or the region of rejection.

The general formula for computing a test statistic for making an inference about a single population is




where observed sample statistic is the statistic of interest from the sample (usually the mean), tested value is the hypothesized population parameter (again, usually the mean), and standard error is the standard deviation of the sampling distribution divided by the positive square root of n.

The general formula for computing a test statistic for making an inference about a difference between two populations is




where statistic1 and statistic2 are the statistics from the two samples (usually the means) to be compared, tested value is the hypothesized difference between the two population parameters (0 if testing for equal values), and standard error is the standard error of the sampling distribution, whose formula varies according to the type of problem.

The general formula for computing a confidence interval is




where a and b are the lower and upper limits of the confidence interval, x is the point estimate (usually the sample mean), test statistic is the critical value from the table of the appropriate probability distribution (upper or positive value if z) corresponding to half of the desired alpha level, and standard error is the standard error of the sampling distribution.

Why must the alpha level be halved before looking up the critical value when computing a confidence interval? Because the rejection region is split between both tails of the distribution, as in a two-tailed test. For a confidence interval at α = .05, you would look up the critical value corresponding to an upper-tailed probability of .025.

Cliffs Notes Online

Featured Local Company

Music/Arts Institute

8168361998
1010 S. Pearl St.
Kansas City, MO

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