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Given that the hypothesis is that the signal is above the noise, the threshold of significance was placed at a one-tailed Z-score of 1.64 (p < 0.05).
We therefore use standard linear regression analysis to test hypothesis 2. Model I in Table 4 shows that in a multilevel model the effect of competition on the level of punishment of defectors is significant (p = 0.074; two-tailed), given that the hypothesis is directional.
Given that the hypothesis is that the signal is above the noise, the threshold of significance was placed at a one-tailed Z-score of 1.64 (p <.05)." For consistency in the revised version of the manuscript, we report the group Z-score at both 1.2 Hz (P8: Z = 12.16, p<.001) and 6 Hz (Oz: Z = 52.9, p<.00001), for both experiments.
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It is true that one cannot conclusively prove a hypothesis, given that the possibility of future counterexamples cannot be rationally excluded.
For example, p-values give the probability of seeing data so at odds with the null hypothesis, given that the null hypothesis is true.
Each of the four measures we assessed in this manipulation study—likelihood of SRE expression, proportional reproductive investment in SRE, total fruits produced, and timing of reproduction showed a trend opposite to what would be expected given that the deferral hypothesis were true, and instead were consistent with the transitional hypothesis.
Given that the null hypothesis of linearity is rejected for alternative specifications of MS-AR models, we follow Franses and Van Dijk (2003) and Franses et al.
In order to simplify and focus our analysis, given that the central hypothesis of our study was the stable bone fixation of these implants, we only used the Merle D'Aubigné and Postel scale, as mentioned above.
The power of a test is the probability of getting a statistically significant result, given that the null hypothesis is false (the flowchart is given in Result part).
Given that the same hypothesis (i.e., significant departure from the null neutral expectation) is tested multiple times on each ORF, we applied Bonferroni's sequential method to maintain an overall 5% significance level [22].
Given that the null hypothesis is true, the P value is the probability of getting a statistical testing value as extreme as the observed instance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com