Changes for page b. Test
Last modified by Demi Breen on 2023/04/09 15:10
From version 26.1
edited by Hugo van Dijk
on 2023/03/30 15:16
on 2023/03/30 15:16
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To version 22.1
edited by Demi Breen
on 2023/03/28 12:22
on 2023/03/28 12:22
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... ... @@ -110,16 +110,7 @@ 110 110 111 111 = 3. Results = 112 112 113 -Firstly, the Jarque-Bera test [2] was used to check for normality. When the answers for a question weren't normally distributed, the Mann-Whitney U-Test [3] was used. For normally distributed answers, the T-Test [4] was used. These tests used the null hypothesis that there is no significant difference between the two groups. When the calculated probability value (p-value) is less than 0.05, we can reject the null hypothesis and conclude that there is a significant difference between the two groups for the answers to that question. 114 114 115 - 116 -Even though the mean rejections were higher for emotion-based (0,875) than for goal-based(0,125). This difference was not significant. 117 - 118 -Furthermore, there was no significant difference in questionnaire answers between the two groups. 119 - 120 -The table below shows the p-value per measure. 121 - 122 - 123 123 = 4. Discussion = 124 124 125 125 ... ... @@ -126,20 +126,4 @@ 126 126 = 5. Conclusions = 127 127 128 128 129 -== References == 130 - 131 131 [1] Brysbaert, M. (2019). How many participants do we have to include in properly powered experiments? A tutorial of power analysis with reference tables. //Journal of Cognition//, //2//(1), 16. DOI: [[http:~~/~~/doi.org/10.5334/joc.72>>url:http://doi.org/10.5334/joc.72]] 132 - 133 -[2] Thorsten Thadewald and Herbert Büning. “Jarque–Bera test and its competitors for testing 134 -normality–a power comparison”. In: Journal of applied statistics 34.1 (2007), pp. 87–105. 135 - 136 - 137 -[3] Nadim Nachar et al. “The Mann-Whitney U: A test for assessing whether two indepen- 138 -dent samples come from the same distribution”. In: Tutorials in quantitative Methods for 139 -Psychology 4.1 (2008), pp. 13–20. 140 - 141 - 142 -[4] Tae Kyun Kim. “T test as a parametric statistic”. In: Korean journal of anesthesiology 68.6 143 -(2015), pp. 540–546. 144 - 145 -