Name: testing preferences with the sign test
Uploaded: 2025-01-09T15:14:08.000Z
Duration: 1 min 12 s
Channel: JoVE

Nominal data, consisting of non-orderable names, labels, and categories, only allows for population proportions analysis.
Given that the data is nominal, the sign test can be used to determine whether there is evidence in favor of a dog or a cat as the preferred pet.
The null hypothesis asserts that the proportion of dog lovers equals 0.5, while the alternative hypothesis suggests that dog lovers outnumber cat lovers.
Dog and cat lovers are represented by positive and negative signs, respectively.
Since n is greater than 25, the z statistic is calculated using the formula provided.
This test is treated as left-tailed because it examines whether the number of negatives is significantly low. At a confidence level of 0.05, the critical value is -1.645.
Since the test statistic is less than the critical value, the null hypothesis is rejected. So, there is sufficient evidence to claim that people are more likely to be dog lovers.

sign test for nominal data

Sign Test for Nominal Data

The sign test is a nonparametric method used to evaluate hypotheses about the median of a single sample or to compare the medians of two related samples. The sign test is particularly useful when dealing with nominal data, which includes distinct categories without an inherent order, such as names, labels, and preferences. Nominal data restricts statistical analysis to evaluating population proportions rather than mean or median values that require continuous data.
For example, consider a survey that queries individuals about their pet preferences, yielding results where a certain number prefer dogs over cats. This scenario presents nominal data because pet preferences are categorical and cannot be ranked. The sign test can then be applied to determine if there&#39;s a statistically significant preference for dogs over cats (or the opposite) among the surveyed population.
In this example, the procedure involves two hypotheses: the null hypothesis (H0), which posits no preference between dogs or cats (assuming an equal proportion of preferences), and the alternative hypothesis (H1), suggesting a significant preference for one over the other. The test uses positive and negative signs to represent preferences for each category. The calculation of the test statistic, often transformed into a z-score for large samples (n &#62; 25), is used to determine if the observed distribution of preferences significantly deviates from what would be expected under the null hypothesis.
If the computed statistic crosses a critical value at a chosen confidence level (e.g., 0.05), the null hypothesis is rejected, indicating no significant preference within the population. This method provides a straightforward approach to testing categorical data for differences, offering insights into population preferences without requiring assumptions about the data distribution.

Nominal data, consisting of non-orderable names, labels, and categories, only allows for population proportions analysis.
Given that the data is nominal, ...

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Statistics

Unit 1

Nonparametric Statistics

KEY TERMS AND CONCEPTS

testing preferences with the sign test

The sign test is a nonparametric method for testing a single sample median or comparing the medians of two related samples. It is useful when the data are nominal, which means the values are categories such as names, labels, or preferences. Because nominal data do not have a natural order, analysis focuses on population proportions instead of mean or median values from continuous data.

A simple example is a survey about pet preference. If some people choose dogs and others choose cats, the results are categorical and cannot be ranked. The sign test can then check whether the population shows a real preference for dogs or for cats.

The test begins with two hypotheses. The null hypothesis, H0, says there is no preference and the two categories should appear in equal proportions. The alternative hypothesis, H1, says that one category is preferred more than the other. Positive and negative signs are used to record the two choices.

The test statistic compares the observed signs with what would be expected under H0. For large samples, usually when n is greater than 25, the statistic is often converted to a z-score. If the result passes a critical value at a chosen confidence level, such as 0.05, the null hypothesis is rejected. This shows a statistically significant preference in the population and gives a simple way to study categorical data without assuming a data distribution.

Testing Preferences with the Sign Test

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The sign test is a nonparametric method for testing a single sample median or comparing the medians of two related samples. It is useful when the data are nominal, which means the values are categories such as names, labels, or preferences. Because nominal data do not have a natural order, analysis focuses on population proportions instead of mean or median values from continuous data. A simple example is a survey about pet preference. If some people choose dogs and others choose cats, the...

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key terms and concepts

Learn how the sign test uses positive and negative signs to test categorical preferences, compare related samples, and check for significant differences in nominal data.

Testing Preferences with the Sign Test | JoVE K12

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Testing Preferences with the Sign Test

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