The investigator, John, was concerned about getting information concerning the data he needed for hypothesis testing to examine whether pulse rate varies among individuals who performed exercises at varying frequencies. John divided the pulse rates into three different groups and conducted a one-way between-groups ANOVA (Nowakowski, 2019). The first group represented high frequency, while the second group represented moderate frequency, and the third group represented low frequency.
|Sum of squares||df||Means square F sig|
- F-ratio for the crowd effect is 0.552
- Sums of squares for the exercise effect is 126.883
- Mean for moderate exercisers 63.442
- The P-value for the exercise effect is 0.579
The results from the one-way ANOVA indicate that the significant p-value is 0.579. The significant p-value exceeds the large p-value of 0.05 hence implying that the null hypothesis is permitted. John would fail in rejecting the null hypothesis because no statistical relationship exists. Therefore, performing Tukey’s HSD test is inappropriate because multiple tests may attract more errors (Nowakowski, 2019). The p-value also accepts that no meaningful difference exists in pulse rate amongst people who perform exercises with low frequency, high frequency, and moderate frequency. 0.552 refers to the calculated F-statistics which exceed the acute region. The capacity of freedom in this research is 2. According to the post hoc test, no significant association exists between the low frequency, high frequency, and moderate frequency groups. The medium frequency (2) and high frequency (1) relationship is 1.00 greater than 0.05. The association between low frequency (3) and high frequency (1) is 0.77 less than 0.05. The association between low frequency and moderate frequency is 0.569.
Nowakowski, M. (2019). The ANOVA method is a popular research tool. Studia I Prace Wneiz, 55, 67-77. Web.