13.4
Prueba de signos para pares emparejados
La prueba de signos para pares emparejados ofrece un método sólido para comparar dos muestras emparejadas, a menudo para determinar los efectos de una…
Considere los datos sobre la velocidad de los hámsters antes y después de administrar un estimulante, que constituye una pareja emparejada. ¿Cómo se prueba la afirmación de que el estimulante afecta la velocidad del hámster?
Dado que se desconoce la distribución de la población, se puede utilizar la prueba de signos, una prueba no paramétrica.
El concepto central de la prueba de signos es que los pares emparejados con medianas iguales tienen aproximadamente el mismo número de signos positivos y negativos.
En primer lugar, convierta los datos brutos en signos restando el valor de la segunda variable de la primera variable. Después de excluir los ceros, se obtienen ocho signos: tres positivos y cinco negativos.
La hipótesis nula postula que la mediana de las diferencias es cero, mientras que la hipótesis alternativa sugiere lo contrario.
Dado que n es menor que 25, el estadístico de prueba x es el número del signo menos frecuente. En la tabla, el valor crítico es cero en un nivel de significación de 0,05.
Dado que el valor del estadístico de prueba no es menor o igual que el valor crítico, no hay pruebas suficientes para rechazar la afirmación de que la mediana de las diferencias es cero.
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Q1: When should you use the sign test for matched pairs instead of a parametric test?
Use the sign test when the underlying population distribution is unknown or cannot be assumed normal. This nonparametric test is particularly valuable for small sample sizes and paired data where you cannot verify normality assumptions. It compares pre- and post-treatment measurements on the same subjects to determine if median values differ significantly.
Q2: How do you convert raw data into signs for the sign test?
Subtract the second variable value from the first variable for each matched pair. Record the sign of each difference as positive, negative, or zero. Exclude all zero differences from the analysis, as they do not contribute to the test conclusion. Count the remaining positive and negative signs to determine your test statistic.
Q3: What do the null and alternative hypotheses represent in a sign test for matched pairs?
The null hypothesis posits that the median difference between matched pairs is zero, implying no treatment effect. The alternative hypothesis suggests the median difference is not equal to zero, indicating a significant treatment effect. The test determines whether sufficient evidence exists to reject the null hypothesis at your chosen significance level.
Q4: What is the test statistic in the sign test for matched pairs?
The test statistic is the count of the less frequent sign (positive or negative) among the paired differences. When sample size is less than 25, you compare this count directly against a critical value from a statistical table at your significance level, typically 0.05.
Q5: How do you interpret the results of a sign test for matched pairs?
If the test statistic is less than or equal to the critical value, the result is statistically significant, and you reject the null hypothesis at 95% confidence. This indicates a significant difference in median values between the paired samples. If the test statistic exceeds the critical value, there is insufficient evidence to reject the null hypothesis.
Q6: Why does the sign test ignore the magnitude of differences between paired observations?
The sign test analyzes only the direction of differences, not their size, making it robust to outliers and extreme values. This approach simplifies calculations and makes the test applicable when you cannot assume specific distributional properties. By focusing on signs rather than magnitudes, the test remains valid even with skewed or non-normal data distributions.
Q7: What advantages does the sign test offer for analyzing paired samples?
The sign test is simple to conduct, requires no normality assumptions, and works effectively with small sample sizes. It is particularly useful when data are ordinal or when the underlying distribution is unknown. The test's simplicity and versatility make it a practical choice for comparing pre- and post-intervention measurements in research studies.