STATS 2

Question 1

What is the basic definition to probability

Answer 1

the likelihood that a particular outcome, of all possible
outcomes will occur

Question 2

if probabilities range from 1 to 0 what do these two numbers mean

Answer 2

0 = never happens
1 = always happens

Question 3

what is the probabilistic reasoning

Answer 3

an absolute statement we think about between reasoning among all people as a real prediction, for ex

Men are taller than women - yes
ALL men are taller than women - no

Question 4

what makes probabilistic reasoning an all statement

Answer 4

by using the phrase all because not all of something con conclude or assume to be another.

Question 5

if A political broadcaster, Eats healthy, works out, yearly stress test. He was told the likelihood of a heart attack was 5% in the next 10 years, Yet died from a heart attack 50 days later.

what is this an example of and why

Answer 5

Collective stat literacy
because as humans we fail to assume that all certainties come with a “what if”

such as a car accident you see it all the time, but you will never think it happens to you.

the percent of likely is a unknown principal we fail to believe may involve us because its a small percentage

Question 6

what is person-who statistics

Answer 6

Individuals who donate
adhere to (or follow) probabilistic trends that lead to a fallacy argument

Question 7

what is an example of a person who stats and why

Answer 7

out of 100%, 95% of people make it by the age of 85 by either two things, because people either don’t smoke or have smoked once and just stopped, continuous smoking causes lifespan to diminish but, there will always be that little percent of people who consistently smoke and still live by 85 and older which is that 5%

Question 8

“Oh, get outta here! Look at old ole Ferguson down at the store. Three packs of Camels a day since he was sixteen! Eighty-one years old and he looks great!”

what is this an example of

Answer 8

Person - who statistics

Question 9

what do we talk about when we mean cognitive illusions

Answer 9

Even when people know the correct answer they may be drawn to an incorrect conclusion by the structure of the problem

Question 10

odd wording to directions or a question we may think we know but in fact get wrong is an example of

Answer 10

cognitive illusion

Question 11

how would we fail to use a sample size

Answer 11

because we fail to look at congestion between two distributions and their limit extremes

Question 12

why would larger samples better than smaller

Answer 12

because we have more room for variability and don’t run into extremes

Question 13

what is the difference between absolute and relative percentages

Answer 13

that if we have a case of 1000 recalled rice, and the case count has gone up from 1 to 2, that is the relative percentage

the absolute percentage is the 1000 cases

Question 14

what is the thinking theory

Answer 14

You fall prey to cognitive absolutes, if you don’t think about what you’re doing you could fail reasoning like failing to think about sampling sizes.

Question 15

what do mean when we talk about the system 1 part of thinking

Answer 15

It is the part of thinking we use the most, we decide without second thought, we operate automatically and quickly, with little or no e ffort and
no sense of voluntary control”

Question 16

what do we mean when we talk about system two part of thinking

Answer 16

allocates attention to the effortful mental activities that
demand it, including complex computations.
associated with the subjective experience of agency, choice, and concentration”

we do not use this part of our thinking often

Question 17

what is the frequentist view

Answer 17

it’s the dominant approach when we look at probability in a long-frequency count because the more trials/options we have the more we find a solution

Question 18

why would we think the frequentist view is ambiguous and objective but also doesn’t allow us to may probability statements

Answer 18

because looking at the long run data may be helpful in finding more reasoning of something happening through pattern, but in the real world patterns don’t determine certaincy especially for single events

Question 19

give me an example as to how the frequent view cannot make probablee statements

Answer 19

when we look at something like weather (singular event) we cannot determine what the weather will be in a week from now regardless of patterns because mother nature is a real-world thing

Question 20

what is the gamballers fallacy

Answer 20

the tendency for people to see links between events in the past and events in the
future when the two are independent”

Question 22

what are simple proabilities

Answer 22

when we look at the probability of one event independently

Question 23

P(data/hypo)

Answer 23

Frequent veiw

Question 23

what is the bayesian view to probability

Answer 23

The most common way of thinking about subjective probability is to define the probability of an event as the degree of belief that an intelligent and rational agent assigns to the truth of a singular event

it be reasonable to use this view as a way to detect the weather

Question 24

P(hypo/data)

Answer 24

Baysian view

Question 25

the statement Based on these data, the evidence in favour of the Alternative Hypothesis is 95.821 times stronger than the evidence in favour of the null hypothesis is an example of what view

Answer 25

the baysian view

Question 26

how are frequentist views and Bayesian views different

Answer 26

the frequentist views follow up probability with t tests and p values more complicated

Question 27

what is a sample place

Answer 27

all the elementary events

Question 28

what is a law of total probability

Answer 28

The probabilities for all events in our sample space must add up to 1.0

Question 28

in a probable distribution all the p sums that equal to one are all dependent of each other (true or false)

Answer 28

false they’re all independent

Question 29

why would p values in a distribution not equal to one

Answer 29

because they wouldn’t be independent p values

Question 30

when would we see discrete data

Answer 30

in binomial distributions such as coin flip tallies
something that has an outcome of failure or success rate no more or less.

Question 31

what makes a normal distribution contious data

Answer 31

because normal distributions give us more options and the results are endless

Question 32

what is the difference between bionomal distribution and normal distribution

Answer 32

binomial distributions only have finite outcomes between success and failure meaning its discrete

normal distributions is infinite with its outcomes making is continuous