@@yesitsme3054 as kiwi as Razor doing the worm after every Crusader trophy win.

@@dr.jackauty4415 Roger 😂😂

Us Kiwis have the best English out there! 😅

Run 😂😂😂

My advice would be in in Rstudio use write.csv(df, "df.csv"). Then just double click on the new file that is created "df.csv" and copy and paste that into Jamovi. The harder way would be in JAMOVI-R use this code df <- read.csv("C:/Users/Document/df.csv") Note you'll have to change the df and the document address to the ones you are using.

Not quite. The seeding and templating process by which small aggregates termed oligomers recruit non pathologically folded forms of the protein to become more problematic is termed "prion like" but it's not fully prion because ingestion of these proteins does not cause templating like a true Prion would enable.

How'd you cure it?

You kind of contradict yourself here. If all you cared about was parameters, then just take the mean of your samples. There's no point doing inferential statistics. If the means of your samples are different, then the means of your samples are different. IF you care about the "real world" then you care about populations, you care if you can repeat your work, you care about if a drug will work in future samples etc. then you need inferential statistics. Your experiment isn't the real world.

Only because you have a prior model of what a bear is and what an ant is. If you gave one patient a drug and it cured them and one patient a placebo and they died, you would not be sure about any future efficacy of the drug. The ONLY reason you know that you could classify ants and bears with an N=1 is because you've seen images of hundreds of bears and ants. So your N is actually hundreds. And of course what about the amazing water bear lol.

@@dr.jackauty4415 "If you gave one patient a drug and it cured them and one patient a placebo and they died, you would not be sure about any future efficacy of the drug." Not "sure", but I would have knowledge. Given that you observe your fellow tribesman die after eating a suspicious plant, would you eat the plant even though N = 1? It's just not AS reliable a sample as observing more people eat that plant, but here we stumble to philosophical territory where the importance of the outcome changes how we should conduct inference. My point is that a datapoint that represents a population is more likely to be empirically similar to other instances of that population than not, and thus even one datapoint tells us something. We just lack variance, which means we can't do fancy sample statistics (because we can't measure error).