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I'm a bit confused with what our manipulation of specific-information was testing

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Could you please clarify how base rate neglect was shown by our experimental design? As we did not compare the data to Bayesian model predictions, how did we measure the extent of this fallacy?

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I'm a bit confused with the interpretation of the significant base-rate relevance finding, but not a significant base-rate finding.

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What was our hypothesis in relation to specific information?

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Did we have to remove any participants from the data?

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In comparison to many studies in the literature, I see very few limitations to our experiment. What are the overarching concerns that warrant mentioning?

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Should we mention that we included a qualitative aspect since we won’t include it in our analysis? I am afraid we will get marked down for saying that we have and then not including it.

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I have conducted a Shapiro Wilk test and saw that the data are not normally distributed. Is there a non-parametric alternative to the 3-way anova? (I have googled this but the views on what to do seem inconsistent)

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Is this study approved by the UCL Ethics Committee and do we need to include a specific ethics approval number within the lab report as well did with the qualitative lab?

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How much was each participant paid for their time and do we need to include this figure in our report?

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differences between male & female participants

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differences between male & female participants

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- Conducting a t-test to compare the mean results of male and female participants was mentioned in the meeting but is this necessary since the distribution of male and female participants is pretty even across all the conditions therefore any differences between the genders would be not make an impact on overall results?
- Also how would we justify this t-test since we don't know for certain whether participants saw Sam as male or female so it can't explicitly tell us about whether the participants believed males are more likely to cheat?

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Should we post them in this padlet throughout the week?

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Should we state the means and sd for each main effect or for each condition?

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Can we have a session next week if we have questions?

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Sorry I still don't quite understand why we take these two figures here. Is that because BBC did report a similar probability for getting cold sores?

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What do the codes represent for gender? E.g. is 1 for female, 2 for male?

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How many words do you suggest us for writing each section? The total word limit is 4000 this time.

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What kind of graph would you suggest be best for this kind of data? And we have quite a few conditions are well so I'm not too sure

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I am not sure which graph to make

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What is the general rule for removing outliers? And is removing outliers a process needed for the data we have?

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If there are significant results, do we need to do post-hoc tests as well?

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Please can you explain how the power analysis was conducted at the bottom of the project proposal? What effect size was it based on?

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As we found non significant results (mostly), what implications did the study provide? is it appropriate to talk about perhaps our results contributing to intuitive theories (as relevance had a main effect, even though an interaction was not found)?

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Hi what is our aim specifically and our hypothesis and what did we expect to see ?