a researcher wants to find out if people who kiss more than ten times a day wear brighter colors to work than those who do not. to test this, the researcher sends a survey to people to ask whether a participant normally kisses ten times per day or not and normally wears bright colors to work. the researcher assumes all assumptions are nonparametric. which test statistic would you use?

The chi-squared statistic can be used to determine whether there is a significant association between the two variables.

What is chi-squared statistic?

When the sample sizes are large, a chi-squared test is a statistical hypothesis test used in the study of contingency tables. To put it another way, the main purpose of this test is to determine whether two category factors have independent effects on the test statistic.

To test whether people who kiss more than ten times a day wear brighter colors to work than those who do not, the appropriate test statistic to use would be the chi-squared statistic.

The chi-squared statistic is a measure of the difference between observed and expected frequencies in a contingency table.

Once we have the contingency table, we can calculate the chi-squared statistic using the following formula:

     [tex]chi-squared = ((observed - expected)^2 / expected)[/tex]

In this case, the null hypothesis is that there is no association between kissing more than ten times a day and wearing bright colors to work. To calculate the expected frequency of each cell, we need to use the formula:

    expected = (row total * column total) / sample size)

Plugging in the values from our contingency table, we can calculate the chi-squared statistic as follows:

     [tex]chi-squared = (10 - 15)^2 / 15 + (20 - 15)^2 / 15\\\\= 25 / 15 + 25 / 15\\\\= 2[/tex]

The chi-squared statistic can be used to determine whether there is a significant association between the two variables.

Refer following diagram.

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