In what ways does the ultimatum address the assassination of Archduke Ferdinand at the hands of Siberian radicals?

At six o’clock in the evening on July 23, 1914, nearly one month after the assassination of Austrian Archduke Franz Ferdinand and his wife by a young Serbian nationalist in Sarajevo, Bosnia, Baron Giesl von Gieslingen, ambassador of the Austro-Hungarian Empire to Serbia, delivers an ultimatum to the Serbian foreign ministry.

According to the terms of the ultimatum delivered on July 23, the Serbian government would have to accept an Austro-Hungarian inquiry into the assassination, notwithstanding its claim that it was already conducting its own internal investigation. Serbia was also to suppress all anti-Austrian propaganda and to take steps to root out and eliminate terrorist organizations within its borders—one such organization, the Black Hand, was believed to have aided and abetted the archduke’s killer, Gavrilo Princip, and his cohorts, providing weapons and safe passage from Belgrade to Sarajevo. The Dual Monarchy demanded an answer to the note within 48 hours—by that time, however, anticipating Serbian defiance, Gieslingen had already packed his bags and prepared to leave the embassy.

Explanation:

if the government does something the people dont like i tihnk threre would be a revolt

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).