Nonparametric Hypothesis Testing Paper

Running Head: NONPARAMETRIC HYPOTHESIS TESTING PAPER

Nonparametric Hypothesis Testing Paper

University of Phoenix

Nonparametric Testing Hypothesis Paper

Nonparametric tests are used when there are one or more unknown parameters. An advantage of nonparametric tests is that they rely more on rank and or sign of the data rather than the actual value of the number (Doane & Seward, 2007). While there are many nonparametric tests that can be used on data, the learners chose the Kruskall-Wallis test. This test is the nonparametric equivalent of the one way ANOVA used in previous research.

Hypothesis Statement

Using prior research, the learners hypothesis that all indicators (life expectancy, literacy, unemployment, and labor force) are all independent of the country. This means that indicators would be equal despite which country they are from. This follows the null hypothesis of μ1 = μ 2 = μ3 = μ4 for the one way ANOVA. Non parametric tests determine dependency. The learners are testing whether the indicators mentioned; life expectancy, literacy, unemployment, and labor force are in fact dependent upon the country they come from or whether they are independent of origin.

Null hypothesis: H0: all indicators (life expectancy, literacy, unemployment, and labor force) are independent of the country of origin.

Alternative hypothesis: H1: indicators (life expectancy, literacy, unemployment, and labor force) are dependent upon the country of origin.

The table used to calculate the test statistic is as follows:

| |Life Expectancy |Literacy % |Labor force |Unemployment |Row Total |

|Australia |79.87 |100 |9.5 |6.4 |195.77 |

|Belgium |77.96 |98 |4.34 |8.4 |188.7 |

|Japan...