Midterm 2 Flashcards

Question 1

Q

Population
Parameter
Statistic

Answer

A

Population
–entire group of interest

Parameter (P - Pop.)
–numerical fact about Population
(MU - ave. GPA of all BYU students)

Statistic (S = Sample)
–numerical fact about Sample (x bar = ave. GPA of 170 students in sample)

Question 2

Q

Sampling distribution of sample mean (x bar) is a theoretical prob. distribution
–it describes the distribution of: …

Answer

A

It describes the distributions of:

-all sample means
-from all possible random samples
-of the same size
-taken from same pop.

MEAN (x bar) = mean of sampling distribution of x bar
SD (x bar) = st. dev. of sampling distribution of x bar

Question 3

Q

relationship btwn sample size and st. dev.

Answer

A

can change n, but if do samples 500x - the mean all becomes SAME - but as N increases, the st. dev. decreases (less variation)
–variation in sample mean values tied to size of each sample - NOT the # of samples

Question 4

Q

Distribution of X bar for ALL possible SRS of size n from a pop. with mean MU and ST. D sigma

Answer

A

-center: MEAN of sampling distribution of x bar is Mu (pop. mean)
-Spread: st. dev. of sampling distribution of x bar is (sigma / radical n)

In real research…not realistic to estimate sampling distribution of sample mean by actually taking multiple random samples from same pop. - but there is a math. machine that shows

–math staticians have figured out how to predict what the sampling distribution will look like w/o actually repeating numerous times and having to close a sample each time

Question 5

Q

Central limit theorem

Answer

A

-if you take a SRS of size n from any pop. then the shape of the sampling distribution of x bar is approx. normal
-shape gets more normal as N increases
n > 30, considered large
-CLT allows us to use st. normal table to compute approx. probability associated with x bar
-The sampling distribution of statistic (like a sample mean) often follows a normal distribution if sample sizes are large

center = Mu = pop. mean
spread = st. dev. = sigma / radical n
shape =
–case 1 - pop. is normal, shape distribution of x bar is normal
–case 2 - pop. is not normal - the shape of distribution of x bar is approx. normal when N is large (> 30) CLT

Question 6

Q

why do we care about the sampling distribution?

Answer

A

-sampling distribution allows us to assess uncertainty of sample results
-if we knew the spread of sampling distribution we could now how far our x bar might be from the true Pop. mean

if sampling distribution has a lot of variance - if you took another sample it is likely to get a very diff. result

-about 95% of the time, the sample mean will be within (z * (sigma / radical n)) of pop. mean
-this tells us how “close” the sample statistic should be to the pop. parameter

Question 7

Q

ex. what is probability that mean of n=75 will exceed $2700? Mu = 2600, st. dev. = 500

Answer

A

(2700 - 2600) / (500/ radical 75) = z = 1.73

-on table pop. = .0418
-have to do the sample st. dev. for the denominator
-if normal than the sample also normal
-so can compute prob. less than 32 - bc normal curve just like pop. - eve if small sample size bc pop. is normal

Question 8

Q

Fill in blanks:

the sample distribution of x bar gives —– from all possible samples of same size from same pop.

Answer

A

the sample distribution of x bar gives ALL X BAR VALUES from all possible samples of same size from same pop.

Suppose we take all possible samples of the same size from a population and for each sample, we compute x̄. The mean of these x̄ values will be exactly equal to the mean of the population (μ) from which the samples were taken - TRUE

Question 9

Q

Suppose we have a very right skewed population distribution where μ = 80 and σ = 20. For random samples of size n = 100, what is the mean of the sampling distribution of x̄?

Answer

A

EQUAL TO 80

–must be equal to pop. mean bc large sample size

Question 10

Q

Suppose we have a very right skewed population distribution where μ = 80 and σ = 20. For random samples of size n = 100, what is the standard deviation of the sampling distribution of x̄?

Answer

A

Less than 20

–large sample size = small st. dev.

Question 11

Q

What is the advantage of reporting the average of several measurements rather than the result of a single measurement?`

Answer

A

The average of several measurements is MORE LIKELY to be close to the true mean than the result of a single measurement
—NOT always = true mean…but more likely

Question 12

Q

Take on sample of size N - compute X bar for sample. as estimate (guess) of Mu
–use knowledge of sampling distribution of x bar in general to say something about uncertainty associated with this x bar of SAMPLE DISTRIBUTION

Answer

A

Center = mean of x bar
Spread = s of x bar = sigma / radical n
Shape = 1. pop. is normal = normal
2. large sample size >30 = normal

Question 13

Q

CHART

Answer

A

if talking about:
1. AN INDIVIDUAL (x)
--pop. of ind. is normal?
= YES
use z = (x - Mu) / sigma
then the normal table to find p
=NO
can't solve stop now

A sample mean (x bar)
–Pop. of ind. is normal???
= YES
use z = (X bar - MU) / (Sigma / radical n)
–then normal table
=NO
check sample size
= > 30 CLT so use (z = (x bar - Mu) / ( sigma / radical n)
–then normal table
n <30 = nothing you can do

Question 14

Q

If all possible samples of size 80 are taken from a population instead of size 20, how would this change the mean and standard deviation of the sampling distribution of x̄?

Answer

A

The mean would stay the same and the standard deviation would decrease

GOOGLE STATS 121 EXAM 2 QUIZLET

Question 15

Q

True or False: We can never compute probabilities on x̄ when the population is skewed.

Question 16

Q

The following scenario applies to questions 3-5:

Suppose we have a normal population distribution where μ = 80 and σ = 20. For random samples of size n = 100, what is the probability of getting an x̄ greater than 75?

Answer

A

.9938

–I did the pop. x bar formula and divided it by sigma over radical 100

Question 17

Q

statistical process control (jelly bean ex.)

Answer

A

series of interconnected steps in producing a product of service (ex. jellybeans)
–rec. raw ingredients - boil to syrup - add flavor - molded into shape - sugar coat - shell - glaze = branded = shipped

Question 18

Q

statistical Dogma for processes

Answer

A

all processes have NATURAL variation
–raw material, human performance, equipment performance, measurement

all processes susceptible to UNNATURAL variation

-bad batch of raw material
broken machine

Question 19

Q

Stats process control 2

Answer

A

use statistical paradigm to monitor process variables (inputs, outputs, etc.) over time to decide if variability consistent with natural variation

-if consistent…continue process
-if inconsistent, stop process - find cause of unnatural variation, fix problem, resume process

Question 20

Q

x bar control chart
in control process
out of control process

Answer

A

x bar control chart
–tool for monitoring variables of a process, alerting us when unnatural variation seems to have occurred

in control process
–process whose output exhibits only natural variation over time

out of control process
–process exhibits unnatural variation over time

Question 21

Q

Use central limit theorem to determine if process is in statistical control

Answer

A

-ex. if jelly belly weight is normally distributed with mean MU = 1.1 grams and sigma = .1, then by CLT:
1. the sampling distribution of x bar is normal with mean 1.1 and st. dec. sigma / radical n

“natural variation” of x bar is within 3 (sigma / radical n) of 1.1

Question 22

Q

constructing control chart for x bar

Answer

A

draw horizontal centerline at Mu (pop. mean)
draw horizontal control limits at
MU +/- 3* (sigma / radical n)
lot the means (x bar’s) from samples of size n against time

Question 23

Q

out of control signals

Answer

A

one point above upper control limit or below lower control limit
run of 9 POINTS IN a row on SAME SIDE of centerline

as soon as out of control signal is observed, STOP the process and look for a cause

Question 24

Q

What is the purpose of a statistical control chart?

Answer

A

To distinguish between natural and unnatural variation

Question 25

Q

inference testing

Answer

A

inference - drawing conclusions about a pop. (parameter) based on a sample (Statistic) with a measure of uncertainty

-everything we’ve learned so far is to ensure valid inference
-making generalizations about the population based on sample data

Pop. 1. producing data from pop.

exploratory data analysis (data from sample)
probability
inference about pop.

Question 26

Q

Types of statistical inference 1. POINT ESTIMATION

Answer

A

POINT ESTIMATION
–quantitative data ex.
based on sample of n = 47 policies, we estimate that the ave. premium is approx. $1800
–categorical data ex.
based on a. sample n = 144 households, we estimate that the proportion of infected bamboo is approx. 10.4%

Question 27

Q

Types of statistical inference 2. INTERVAL ESTIMATION

Answer

A

INTERVAL ESTIMATION
–quant. data ex.
based on sample of n = 47 policies, we estimate that the ave. premium is btw $1700 and $1900
–categorical ex.
based on sample of n = 144 households, we estimate that the proportion of infected bamboo is btw 8.4% and 12.4%

Question 28

Q

Types of statistical inference 3. HYPOTHESIS TESTING

Answer

A

HYPOTHESIS TESTING
–Quant. ex.
insurance agent believes that the ave. premium at her agency is $2500. Based on the sample n = 47 claims, we found that the ave. premium was $1800. this data, therefore, provides evidence that the ave. premium is less than $2500. That is, x bar = $1800 is an outcome that would rarely happen if the ave. was indeed $2500.
–categorical ex.
researchers believe that the proportion of infected bamboo is 2%. Based on a sample of n = 144 households, found that prop. was 10.4% This data provides evidence that the prop. is > 2%

Question 29

Q

Point estimation definition

–estimator and estimate

Answer

A

ESTIMATOR

-general statistic that estimates the parameter
-ex. the estimator of the pop. mean MU is the sample mean of x bar

ESTIMATE

-a specific value of an estimator
-ex. the ave. value of n = 47 is $1800
the prop. of infected boards for n = 144 is 10.4%
-the estimator of the pop. proportion p is the sample proportion p (with hat)

Question 30

Q

how good is the estimator x bar for MU?

Answer

A

A.) sampling needs to be done RANDOMLY

B.) sampling distribution of x bar tells us:

on ave. x bar will give us the right answer - Calle this property UNBIASEDNESS
As sample size n INC. the accuracy of x bar INC. (smaller st. dev)

Question 31

Q

Statistical inference chart

Answer

A

CONFIDENCE INTERVAL point estimate +/- ME
Test of significant res. fail to rej. at level alpha

both solve for:

-conclusion about parameter
-measure of uncertainty

LOOK AT CHART - CH. 17

Question 32

Q

four steps for confidence intervals

Answer

A

STATE
–Specify parameter of interest

PLAN
–choose procedure, level of confidence

SOLVE
–check conditions, carry out procedure

CONCLUDE
–interpret confidence interval

LOOK AT MY WRITING ASSIGNMENT

Question 33

Q

four steps for tests of significance

Answer

A

STATE
–Specify claims about parameters of interest

PLAN
–choose procedure, specify Ho, Ha, alpha

SOLVE
–check conditions, calc. test statistic and p-value

CONCLUDE
–compare p-value to alpha, interpret test results

Question 34

Q

What do we use to estimate μ?

M (aka median)

Question 35

Q

A recent study claimed that half of all college students “drink to get drunk” at least once in a while. Believing that the true proportion is much lower, the College Alcohol Study interviews an SRS of 14,941 college students about their drinking habits and finds that 7,352 of them occasionally “drink to get drunk”. What type of statistical inference is this?

Answer

A

Hypothesis testing

Question 36

Q

Inferences for Mu (in unrealistically simple case)

Answer

A

-conclusion about MU
-gather data using SRS
-Sigma known (rarely known for pop.)
-sampling distribution of x bar is normal bc.
1. pop. is normal or
2. large sample, due to CLT

construct 95% confidence interval
–Plan to gather a random sample of 100 students. x bar estimates mea systolic blood pressure of all students

parameter: mean systolic blood pressure of all students
- -sample size: n = 100
- -st. dev. - sigma = 14.0 mm HG

how close should x bar be to Mu? informal distribution. then by 68,95,99.7 rule we cam say:
–the prob. that x bar is w/in 2 st. dev. (214/ radical 100 = 2.8) of Mu is .95
OR we are 95% confident that Mu is within 2(14/radical 100) of x bar or that interval x bar +/- 2* sigma/radical n contains MU

CAN’T SAY “PROBABILITY” WHEN TALKING ABOUT MU, So use “confident”

ex. n = 100, x bar = 123.4, sigma = 14
confidence interval can b written as
(120.6, 126.2)
OR (120.6 - 126.2)
OR (120.6 to 126.2)

Question 37

Q

FORMULA FOR A 95% CONFIDENCE INTERVAL

Answer

A

X bar +/- 1.96* sigma/radical n

the 1.96 uses table of st. normal prob.and corresponds to the MIDDLE 95% of normal distribution

use confidence interval to fin z. and c% confidence int. for MU
–genreal formula for c% cond. int.

X bar +/- z* (sigma/ radical n)

z+ = confidence level you want

z is found at bottom of C TABLE

Question 38

Q

IF Sigma (pop. st. dev.)is UNKNOWN
= one sample t confidence interval for means

Answer

A

-We replace sigma with s
-we replace z* with t*

bar +/- t* s/ radical n
t* = level of confidence you want

IF data gathered using SRS

-Sigma unknown
-normality of pop. distribution or large sample size

then:
sampling distribution of (x bar - mu) / (s / radical n) has a student’s t-distrib.
–WITH n-1 degrees of freedom DF

Question 39

Q

properties of t-distribution

Answer

A

-symmetric, bell-shaped, mean = 0
-the smaller the DF the larger the spread (bc more uncertainty due to s)
-the larger the DF, the closer the t-distrib. to the standard normal

Question 40

Q

format of table of t-distrib.

Answer

A

each t distribution is determined by its DF: df = n - 1
if actual DF is not on table, use DF closest to actual without going over
t* values are found in bod of table C

Question 41

Q

one sample t confidence interval outline

Answer

A

STATE problem
PLAN
- -Select procedure: one sample t CI for means
- -select confidence level
- -select parameter of interest in context
SOLVE
–collect and plot data
–calc. c bar and s
–check conditions (randomness and normality of pop distribution/ large sample size w/ no outliers
–calc. confidence interval using formula:
bar +/- t* (S / radical n)

4. CONCLUDE
interpret CI in context including
--confidence level
--parameter of interest
--calculated interval

Question 42

Q

EX. One sample t conf. int. for MU

Answer

A

STATE: students at large church activity took survey of 121 married students to estimate 2/ 90% confidence how long these students had dates on ave. before getting engaged
PLAN: use “one sample t CI for MU
- -confidence level:90%
- -parameter of interest: mean # of months married students dated before engagement
SOLVE: collected SRS of 121 married students
s = 10, x bar = 9.34
–conditions: random (SRS)
–normal or large sample size, n = 121
(1st CHECK IF CONDITIONS MET..IF ARE CAN CONT. AND FIND CI)

9.24 +/- (1.66) * (10/radical 121) =
= (7.83, 10.85)

CONCLUDE:
- -we are 90% confident (NOT PROBABILITY) that the interval (7.8 mo, 10.8 mo) contains the TRUE MEAN # of mo. married students at tis activity dated before getting engaged

Question 43

Q

definition of confident

Answer

A

in repeated sampling, the confidence level is the percentage of confidence interval produces by procedure that actually contain the value MU, or the success rate of the procedure

ex. 95 of all possible 95% conf. interval estimates for MU actually contain the value of MU
- -the confidence is based on the PROCEDURE, not the INTERVAL

Question 44

Q

True or False: Increasing the sample size will lead to a wider margin of error.

Answer

A

false

Suppose a sample of size 250 was taken instead of size 100. How will the margin of error change?
–margin of error would DECREASE

Question 45

Q

True or False: Increasing the confidence level will lead to a wider margin of error.

Question 46

Q

Which one of the following is the correct representation of the margin of error when sigma is unknown?

Answer

A

t* sigma/ radical n

MARGIN OF ERROR DOES NOT INCLUDE THE SAMPLE MEAN
just the second part of equation after =/-

Question 47

Q

What is the name of the quantity (s/ radical n?)

Answer

A

standard error of x-bar

Question 48

Q

breaking up the formula

Answer

A

x bar +/- z* sigma/radical n

-x bar = point estimator
-z* = confidence multiplier
-radical n = st. dev. of pt. estimator
-everything RIGHT of +/- is MARGIN OF ERROR (m)

as sample size INC, margin of error DEC.
-AS N INC, width of confidence. interval DEC.

–confidence level INC, margin of error INC

also can be written as x bar +/- m.

Question 49

Q

margin of error

Answer

A

margin of error (m) controls the width of the interval
as sample size INC, m and width DEC
as conf. INC, m and width INC

m = (z*sigma)/ radical n

ex. sigma = .25, m must be no larger than .05 with 99% confidence
- -how many times should PH be measured?

n = ((z* sigma)/m)^2 = 165.89 = 166
–ALWAYS ROUND UP TO NEXT INTEGER (even if 165.1 — 166).

Question 50

Q

a 90% confidence int. is (.21, 2.45) what is the margin of error?

Answer

A

(.21 + 2.45)/ 2 = 1.33

33 - .21 = 1.12
45 - 1.33 = 1.12

M = 1.12!!!
–Margin of error is the distance from lower bound to mean and upper bound to mean

Question 51

Q

standard error of x bar

Answer

A

= s / radical n
–not just S!!!!!

S/ radical n = estimates the st. dev. of the sampling distribution of x bar

Question 52

Q

In confidence interval estimation, we use a confidence interval to estimate ———-

Answer

A

the value of a population parameter

Question 53

Q

The t-distribution with 6 degrees of freedom has ____________________ the standard Normal distribution.

Answer

A

t disturb. has the same center but is more spread out than st. normal distribution

Question 54

Q

The purpose of a confidence interval is to provide

Answer

A

plausible values that a parameter could take

–NOT a measure of the confidence we can have in our sample results representing the population

Question 55

Q

hypothesis testing

Answer

A

draw conclusion about PARAMETER using STATISTIC
WITH a measure of uncertainty
confident interval = estimates a parameter
test of significant = hypothesis testing = to assess a claim about a parameter’’

“significantly reduced” = mean score was TRULY reduced

-can prove it is NOT true but can’t prove it is
-we failed to disprove a claim, can’ prove it is true but can prove life its not

ALWAYS assume claim that researchers think is NOT true

-approach called PROOF by CONTRADICTION (can’t disprove so can argue it is true)
-researchers think claim is FALSE so they assume it was TRUE

Question 56

Q

4 elements o test of significance

Answer

A

CLAIM 1: (Ho) - Null hypothesis - always be explained in problem as ___ = ____
CLAIM 2: (Ha) - alternate hypotheses - always be ___ , not = ___
OUTCOME - standardized outcome that measures how far the outcome diverges from CLAIM 1 (p-value)
- -outcome represented by standardized stat. called a “Test stat” (p-value)
ASSESSMENT OF EVIDENCE
- -how likely is it to get this outcome if claim 1 is true?
- -p-value - prob. will get something in the tail
CONCLUSION
- -an outcome that would rarely happen if claim is true is good evidence that Claim 1 is not true, hence we believe claim 2 is true

Question 57

Q

stats about significance test

ALPHA meaning

Answer

A

related measure of uncertainty is alpha which represents the prob. of falsely rejecting claim 1 or Ho : alpha defines what is rare or unlikely outcome

a test with >/< in Ha is a ONE-SIDED TEST

test with NOT = is a two-sided test

Question 58

Q

test statistic

Answer

A

number that summarizes data for a test of significant

-companies an estimate of parameter from sample data with value of parameter given in null hypothesis
-measures how far sample data verge from Ha
-large va are not consistent with Ho: evidence against Ho
-used to find prob. of obtaining sample data IF Ho were true
-ex. of test statistic

t = bar - Mo /(s / radical n)

Question 59

Q

meaning of p-value

Answer

A

a # btw o and 1
0 <= P-val. <= 1
-the prob. of getting test stat as extreme or more extreme than observed if Ho were true
–measure of strength of agreement btw observed test stat and Ho
–measures evidence against HO

STRENGTH OF EVIDENCE AGAINST Ho - PVAL IS THE PROBABILITY THAT H0 IS TRUE!! LOW PROB. MEANS SHOULD REJECT
Pval close to 0 = good evidence H0 is not true = evidence for Ha

Pval - .5

p val. = 1
no evidence that Ho is not true
= not evidence for Ha

Question 60

Q

pre-specified cut-off for P-val.

Answer

A

artificial but imp. - sharp boundary btw rejection and non-rejection regions for p-value
–if p-val. <= alpha - diff. is statistically significant, reject Ho and conclude it as false

ALPHA = ASSUME .05 UNLESS SPECIFIED IN PROBLEM

Question 61

Q

Alpha and P-val.

Answer

A

P-val. <= alpha: REJECT Ho

-declare observed diff. statistically significant
-conclusion: believe Ha
-diff. btw claimed parameter value and calculated statistic likely real, not chance
-stat tests do not address issue of importance (“practical significance”

P-0val > alpha: do not reject Ho

-do not declare statistacally sign.
-insufficient evidence to believe Ha
-don’t accept Ho but fail to reject Ho
-fail to rejectHo means the diff. could be due to chance, not real

Question 62

Q

Alpha definition

Answer

A

risk of false positive (reject null when actually true)

–risk should generally be small(

Question 63

Q

T/F the NULL hypothesis is the claim that the researcher ants to prove

Answer

A

FALSE

–Trying to prove the alternate hypothesis

Question 64

Q

one-sample t test examples

Answer

A

realistic case:

-gather data using SRS
-sigma unknown
-pop. distributions. approx normal (Single peak, no excessively long tails)

if sample does not have extreme outliers or skewness, can assume normality or pop.
as n INC. skewness and non-normality less worrisome

replace sigma with s
x bar - Mu / (s / radical n)

st. dev. = radical (sum of (x - x bar)^2 / n-1)

if sigma unknown, random and normal/;are sample size
then: sampling distribution. of x bar - Mu / s/ radical n has a t-distrib. with n-1 DF

t = (x bar - mu) / (s / radical n)
95% conf. interval = x bar +/- s / radical n

Answer 62

A

false positive: rejecting null hypothesis when shouldn’t have

false negative - failing to reject null hypotheses when you shouldn’t have

TYPE 1 error: reject Ho when it is true: false positive - pronounce guilty when they are innocent

TYPE 2 error: fail to reject Ho when it is false - false negative - pronounce innocent when guilty

type 1 is more serious - put innocent person behind bars WORSE than letting guilty person roam free (type 2)

Answer 63

A

alpha = level of significance

-probability of type 1 error
-probability reject Ho when it is true

BETA

-probability of type 2 error
-probability fail to reject Ho when it is false

POWER = probability (reject Ho when it is false)
1 = beta
--power = .99 = 99% prob. of rejecting null hypothesis when it is actually false (want to be high!!)

LEARN THE GRAPHS!!!

Answer 64

A

N INC. the sample distribution. gets tighter and narrower

larger sample size always INC. power

alpha = prob. type 1 error
Beta = prob. type 2 error

power = prob. of rejecting Ho when it is false (with beta)

effect size = diff. btw Ha and Ho
(EX. mu = 28, x bar = 24, effect size = 4)

Answer 65

A

10 is harder - more data = easier

Answer 66

A

Type 1 error
Alpha
Reject Ho
TRUE

Type 2 error
Beta
Fail to reject Ho
False Ho

Answer 67

A

effect size - # change in Ho and Ha
variability in measurements (sigma = out pop. st. dev. - inc. var = dec. power, dec. sigma = inc. power)
chosen significance level (alpha)
sample size (N INC = power INC (bc dec. st. dev.)

Answer 68

A

t = x bar - Mu / (s / radical n)

significance depends on:
x-bar mine Mo:
–size of observed effect(numerator to test stat.)
–measures how far the sample mean deviates from the hypothesized Mo
–the “LARGER” the observe effect, the smaller the p-val. (which tests the prob. Ho should be accepted)
–size of sample n
–s/radical n - measures how much random variation we expect
–larger sample size = smaller p-val.
–sample size may be too small to detect significance
–sample size may be so large results are always significant

Answer 69

A

results are declared STATISTICALLY SIGNIFICANT when P-val. <= alpha

Results practically important when observed effect (numerator of test stat.) is large or imp. enough to mater

practical importance is not same as statistical significant
–practical im. determined by common sense

large samples - unimportant diff. can be significantly significant

small sample - important diff. may not be statistically signifiant

–always ask whether stat. significant effect is “large enough to matter”

Answer 70

A

declare statistically significant if P-val. <= alpha

-consider practical. imp. when observed effect large - practical value
-observed effect: numerator of west stat (x bar - Mo)

1st. check for STAT. SIGNIFICANCE
2. d = Check PRACTICAL IMP.

Answer 71

A

2 * p-val. of one sided test - 2 sided req. stronger evidence than one-sided

Answer 72

A

conduct test of significance at alpha = .05
–Ho: mean egg weight = 50g
–Ha = mean egg weight does not = 50 g
compare p-val. with alpha and draw conclusions
–find t = (x bar - Mo)/ (s/radical n) then look at DF for that 2 and make an interval
inspector interval
- -construct 95% CI for Mu
- -see if interval includes or excludes value 50
- -if CI includes 50: data provides support for Ho
- -if CI excludes 50: data provides evidence to reject Ho

x bar +/ t* s/ radical n

Answer 73

A

FALSE
–A p-value is a conditional probability—given the null hypothesis is true, it’s the probability of getting a test statistic as extreme or more extreme than the calculated test statistic

Answer 74

A

How likely it is that, in a sample of 250, we will find that the mean number of hours per week full-time corporate employees work is as high as 47 if the true mean is 40?

Answer 75

A

Assuming the null hypothesis is true, there is a 0.0367 probability of obtaining a sample statistic as extreme or more extreme than what we calculated.

Reject the null hypothesis. The true mean hemoglobin level of all children in Jordan is less than 12 g/dl.

Answer 76

A

Fail to reject the null hypothesis. There is insufficient evidence to conclude that the mean hemoglobin level of all children in Jordan is less than 12 g/dl.

NOT - Fail to reject the null hypothesis. The mean hemoglobin level of all children in Jordan is equal to 12 g/dl - can’t accept!! But know Ha not true

Answer 77

A

Increase sample size.

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Midterm 2 Flashcards

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