Pearson Chi-Square Test
Please ensure that the Discussion includes more than 400 words with scholarly articles (include at least two scholarly references), and the plagiarism level must remain below 20%.
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You must pick only ONE of the topics mentioned below for your discussion post this week:
1.Understanding Pearson chi-square test
2.Examine tools used to evaluate significance
3.Explain how to disseminate research findings
Understanding
The Pearson test is a widely used non-parametric statistical tool employed to determine if there is a significant association between two categorical variables. It helps researchers assess whether observed frequencies in a contingency table differ from the frequencies expected by chance under the null hypothesis of independence (McHugh, 2013). Unlike parametric tests, which assume a specific distribution (usually normal), the chi-square test requires fewer assumptions, making it versatile for categorical data analysis in fields like healthcare, social sciences, and marketing.
Pearson Chi-Square Test
The fundamental principle of the chi-square test is to compare observed counts in each category to the expected counts, which are calculated based on the assumption that the variables are independent. The formula for the test statistic is:
χ2=∑(Oi−Ei)2Ei\chi^2 = \sum \frac{(O_i – E_i)^2}{E_i}
where OiO_i is the observed frequency, and EiE_i is the expected frequency for each cell in the contingency table. A larger chi-square value indicates a greater divergence from the expected distribution, suggesting a possible association between variables.
If the test statistic exceeds the critical value, the null hypothesis of independence is rejected, indicating a statistically significant relationship.
However, the Pearson has limitations. For instance, it requires sufficiently large sample sizes to ensure validity because expected frequencies should generally be five or more in each cell (McHugh, 2013). When sample sizes are small, alternative tests like Fisher’s exact test may be more appropriate. Additionally, while the test can indicate an association, it does not imply causation or the direction of the relationship…………………
