What does paul duncum mean by intertextuality?

1- in bradbury's  fahrenheit 451, all books are banned and burned upon discovery. in the nazi germany, books considered to be subversive to the regime would be burned to keep the population from generating ideologies that could challenge nazism.

2- was bradbury correct in his literal interpretation of  “where they burn books, they will also ultimately burn people.”? in some way, yes. when knowledge is not only limited, but also controlled, the people tied to it are led to their demise. just like in the novel, people in nazi germany who owned  subversive books were punished not only by the burning of their property, but also by the discrimination that followed after such a public form of consequence. if a person was seen being confiscated  subversive  books, they may be considered as an ally to the jews, which meant pretty much the same as being the enemy back then. because of the extreme exclusion and suffering that followed this punishment, it's almost like dying a slow death.

in this way, the phrase  “where they burn books, they will also ultimately burn people.” is both literal and figurative.

explanation:

mark brainliest and have a great day!

a hypothesis is an expected answer to a question, usually based on some theory or prior research. hypothesis testing is a method of determining the viability of a hypothesis. let’s use the us presidential election as an example. there are many hypotheses (questions) about how people feel or whom they will vote for. the only way to know the 100% “truth” about the answers to these questions is to ask every single possible voter – something that is impossible, expensive, time consuming etc. instead, we take samples and use statistics to make “best guesses” about the answer to a particular hypothesis. we never know for sure if our conclusion is right at the time we accept/reject it, but these techniques, when used appropriately can get us close to the truth most of the time (which is a lot better than guessing, flipping coins or throwing darts to get answers).

the tricky part about hypothesis testing is that we have to think somewhat backwards about it – we have to test what is known as the “null hypothesis”. as an example, the hypothesis might be “obama is more popular than romney” while the null hypothesis is “there is no difference in popularity between obama and romney”. we focus on the null hypothesis because it tells us exactly what the expected value of our tests should be (obama popularity = romney popularity) and we can collect data and see if it comes out that way (see next section on “significance”). if it does, then we “accept the null hypothesis” and reject our original hypothesis. if the null hypothesis is disproven, we look at the data to see if our original hypothesis holds (obama> romney).