A hope that helps have a good
Fewer
Increase due to people making less money
Lose there jobs and money
Decrease
Less
Decrease
Answer: Another leading force was imperialism, the process of a stronger country taking over a weaker country or territory. ... To get these countries at high demand, powerful armies were needed, eventually leading to militarism, glorifying military power and have a standing army at all times.
I believe the answer is: <span>in the nest; increasingly common
In 2012, the number of young adults who are still living with their parents are about 20%. In the year of 2017, the number has been increased to a staggering 35% and showing no signs of it ever gonna come down to lower percentage.</span>
Answer:
B) The chance of committing a Type I error changes from 0.01 to 0.05., E) The test becomes less stringent to reject the null hypothesis (i.e., it becomes easier to reject the null hypothesis), therefore. C) It becomes harder to prove that the null hypothesis is true and G) The chance that the null hypothesis is true changes from 0.01 to 0.05 are all correct answers
Explanation:
The alfa error or type I, refers to the probability error of rejecting the null hypothesis, when it is true, it is the chance of mistake when affirming that an association exists between two variables tested as a cause of an effect on something. i.e: H1: fast food is responsible for diabetes (this is working hypothesis), H0: red hair is responsible for diabetes (this is the null hypothesis). The beta error or type II is related to the size of the sample, it is the chance of accepting something (the null hypothesis) when it is false, depends mostly on having enough measures (or persons under study) so your hypothesis can be proven and be a real representation of the population under study. The statistical significance, namely the p value, can be narrow (p=0.01) or wide (p=0.05), it can be easily understand if we explain it in terms of percentage: you can have 99% (p=0.01) of certainty to affirm that the null hypothesis (the one that you do not believe is true, in the example, red hair as cause of diabetes) is actually wrong or 95% (p=0.05) of certainty to affirm that the null hypothesis (again, the one that you do not believe is true) is actually wrong.
