## Biostatistics MCQs

Question
Option A
Option B
Option C
Option D
Correct Option
For rare events ……. Probability distribution is used
normal
poisons
binomial
continuous
2
The trials which results in either success or failure are are knows as
experiments
probability trials
Bernoulli trials
normal trials
3
The chance of occurrence of an event in random trials is called as
frequency
variable
probability
probability distribution
3
Set of all possible outcomes of an random experiment is called as
sample space
favorable events
exhaustive events
null events
1
In tossing a coin for one time,the events head and tail are
sure events
null events
equally likely events
impossible events
4
In a trials if there are only two outcomes are possible , then for such trials ……. Probability distribution is used.
normal
poisons
binomial
none of these
3
Conditional probability is used for
dependent events
independent events
sure events
null events
1
Formula for addition law of probability is
p(AUB)=P(A)/P(B)
P(AUB) = P(A)+P(B)
P(AUB) = P(A)XP(B)
P(AUB) = P(B)/P(A)
2
If two or more than two events can not occur simultaneously, then such events are called as
mutually exclusive events
dependent events
impossible events
independent events
2
The curve of continuous probability distribution is always
bell shaped
symmetrical
Both A and B
asymmetrical
3
The probability distribution of discrete variable is usually represented in
graphical form
formula form
tabular form
None of these
2
Real number associated with the outcomes of an random experiments is called as
random variable
probability
value
none of these
1
Number of insect , number of plant etc. are the example of variable
continuous random
discrete event
Both A and B
none of these
3
In case of normal probability distribution
mean < variance
mean = variance
mean = median = mode
variance > mean
3
Continuous random variable is used to represent
countable data
measurable data
Both A and B
none of these
4
Plant height , seed weight etc. are the example of ……… variable
continuous random variable
discrete random variable
Both A and B
none of these
1
If X follows N(μ,1) then mean and variance is
0 and 1
1 and 1
μ and 1
none of these
3
Parameter of binomial distribution is
n and p
p and q
only p
only n
1
Relation between mean and variance of poison’s distribution
mean < variance
mean = variance
variance > mean
mean = 2variance
2
If x follows expxo(5) , the probability density function of x is
5e-5x for x>0
e-5x for x>0
5e-x for x>0
1/5 e-5x for x>0
1
The range of normal distribution is
0 to 1
–finity to + infinity
0 to infinity
– infinity to 0
2
if x follows G(λ,n) mean of gamma distribution is
n/λ
λ/n
n
2
which distribution is continuous distribution
Poisson
binomial
normal
none of the above
3
mean and standard deviation of the series 4,4,4,4,4,4,4, are
0 and 4
4 and 0
4 and 4
0 and 0
2
which of the following are probability vectors
0.5,0.5
0.5,0.5,0.5
0,0,0,1,0
1,6,1,2,1,3
1
Which of the following is not correct
an absorbing state is recurrent
an ergodic state is recurrent
recurrent state is periodic
an absorbing state is aperiodic
3
for a markov chain xn with state space S.pij=p[xn+1=j/xn=i] for all i,j€S,then
pij are called n step transition probabilities.
pij are called (j-i) step transition probabilities.
pij are called step transition probabilities of order n
pij are called one step transition probabilities from state I to j.
If in a block the number of units is less than the number of treatment s, then the block is said to be
complete
incomplete
unit < treatment,block
insufficient block
2
{Xt,t€T} is a stochastic process. If the joint distribution of xt1,xt2,........xtn and Xt1+h,xt2+h.........xtn+h. Is same for all h>0;then x(t) is
weak stationary process
strong stationary process
process with independent inccrements
Markov process
4
) If arrival are according to Poisson process then distribution of inter arrival time is,
gamma
chi-square
exponential
normal
1
If {N1(t)} and {N2(t)} are two independent Poisson process with rates t1 and t2 respectively then N1(t) –N2(t) is a ...
Poisson process with rate t1+t2
Poisson process with t1-t2
Poisson process with t1/t2
Not a Poisson process
1
If p{xn+1=k/xn=j} =pjk its mean means
one step transition probability
two step transition probability
m- step transition probability
none step transition probability
1
Condition of transition matrix is
pjk is greater than equal to zero
sum of pjk = 1 for all j
sum of pjk= 1 for all k
all of the above
4
The possible value of random variable xn of stochastic process {xn;n>1} is called ...
state
state space
both a and b
none of the above
1
) {Xn;n= 0,1,2,3,.....} is a example of .....
continuous state space
discrete state space
both a and b
none of the above
2
State j is said to be accessible from state i, is called
accessibility
transitivity
class of state
any of the above
1
If C(i) not equal to S then. where C is a proper closed subset of sets.
irreducible Markov chain
reducible markav chain
both a and b
none of these
2
which of the following statement is wrong
a finite markav chain can not have all transient
a finite irreducible chain has all state recurrent
both are wrong
both are true
4
When data are homogeneous the which test is useful
t- test
F- test
chi - Square test
Z- test
3
) Calculated value of chi-square is greater than the tabulate value then
hypothesis is accept
hypothesis is reject
hypothesis is accept as well as reject
any of the above
2
for a symmetric random walk,probability p of positive jump is
0.25
0.5
1
none of these
3
The state space and time domain for random walk model are ..
discrete and discrete
discrete and continuous
continuous and discrete
continuous and continuous
3
Recurrent state is also called as ...
a) ergodic
Persistent
transient
none of these
1
All entries of transition probability matrix are always ....
positive
non-negative
integer
none of these
2
The major difference between the chi-square test of homgenenity and the chi-square test of independence is the
number of categories
sample size
method of sampling
size of chi-square statistics
3
which test is appropriate for determining whether a random digit generator is truly random in terms of the proportion of each digit is produces
the chi-square goodness of fit test
the chi-square test of homogeneity
the chi-square test of heterogeneity
None of the above
2
Which of the following is not a necessary condition of hardy-weinberg equilibrium
no natural selection
migration of individuals
large population
random mating
2
) All of the following are important aspect of hardy-weinberg equilibrium Except
mating patterns
population size
migration
temperature
4
which of the following conditions are required for a population to be in hardy-weinberg equilibrium ?
large population
no gene flow between population
random mating
d) all of these
4
The genotypic ratio of a monohybrid cross is
1:2:1
3:01
2:01:01
9:3:3:1
1
if a plant with genotype AaBb is self-fertilized,the probability of getting AABB genotype will be (A and B are not linked)
2-Jan
1/4
8-Jan
1/16
4
The tendency of an offspring to resemble its parent is konw as
variation
heredity
resemblance
inherit deance
2
9:7 ration in the F2 generation represents
incomplete dominance
co- dominance
epistasis?
complementry interaction
3
Under what conditions randomised design is a suitable approach of experiment design
a) population is large
when population is highly heterogeneous
when population is approximately homogeneous?
all of the above
3
Which statistical measures in ANOVA?
z score
t value
F ratio
chi-square
3
Which of the following statistical measurement that must be included while reporting an ANOVA
F- Statistics
dregrees of freedom and p- value
c) means
all of the above
1
How many dependent variable are present in two way ANOVA
2
3
1
5
3
which of the following statement are true with regard to the analysis of variance for two population
they have identical variance
they should follow normal distribution
both a and b
none of the above
3
analysis of variance is astatistical method of computing the ..... Of several population
standard deviations
variance
proportion?
none of these above
3
The ......sum of square measure the variability of the observed value around their respective treatment mean.
treatment
error?
interaction
total
2
when conducting an ANOVA ,F will always fall within what range
between negative infinity to infinity
between 0 to 1
between 0 and infinity?
between 1 to infinity
3
If F ratio is 0.9 the result is statistically significant
always
sometimes
never
none of these
4
As variability dhe to the cahnce decrease ,the value of F will be
increase
stay the same
decrease
can't tell from the given information
1
The error deviation within the SSE statistic measure distance
within group
between group
both a and b
none of the above
1
If X~b(n,p) the distribution of y= (n-x) is
b(n,1)
b(n,x)
b(n,p)
b(n,q)
4
A family of parametric distribution having mean <,=,> variance is
gamma distribution
exponential distribution
logistics distribution
all the above
4
For a normal curve, the Q.D,M.D and S.D are in the ration
5:06:07
10:12:15
2:3:4
none of the above
2
) X is a binomial variate with parameters n and p.if n=1,the distribution of X reduces to
Poisson distribution
binomial distribution
Bernoulli distribution
discrete uniform distribution
3
A box contain 12 item out of which 4 are defective . A person selects 6 item from the box.the expected number of defective number of defective items out of selected items is
2
3
3/2
none of the above
1
If x~(8,64), the standard normal deviate z will be
X-64/8
X-8/64
X-8/8
8-x/8
3
If a random variable X has mean 3 and standard deviation 5, then the variance of the variable y= 2x-5 is
25
45
100
50
3
if binomial random variable has mean= 4 and variance = 3, then its third central moment is :
1/9
b) 2/3
5/12
1/3
4
A Poisson random variable has fourth moment is 4, the value of its mean is
a) 1/3
b) 2/3?
c) 1/4
3/4
2
A normal random variable has mean is 2 and variance is 4. Its fourth central moment will be
16
64
80
48
4
) let X~N(mean,variance ) then the central moments of odd order are
one
zero?
infinite
positive
2
The number of parameters in a multinomial distribution having k classes and n observation is
n+1
K+1
n-k
n+k
2
The outcome of tossing a coin three times are a variable of the type
continuous random variable
discrete random variable
neither discrete nor continuous random variable
discrete as well as continuous
2
The height of person in a country is random variable of the type:
continuous r.v
discrete r.v
neither discrete nor continuous r.v
continuous as well as discrete r.v
1
) A table with all possible value of a random variable and it's corresponding probability is called
probability mass function
probability density function
cumulative distribution function
probability distribution
4
Out of the following values, which one is not possible in probability
0.2
0.9
c) 0.5
-0.5
4
Probability is expressed as
ratio
proportion
percentage
all the above
4
Classical probability is also konw as
laplaces probability
mathematical probability
a priori probability
all the above
4
The probability of all possible outcome of a random experiment is always equals to
infinity
zero
one
none of the above
3
If A is an event, the conditional probability of A given A is equals to
zero
one
infinite
indeterminate quantity
2
If an event B has occurred and it is konw that p(B)= 1, the conditional probability p(A/B) is equal to
P(A)
P(B)
one
zero
1
the probability of two persons being borned on the same day is
a) 1/49
b) 1/365
c) 1/7?
d) none of the above
3
Probability of impossible event is
1
0
-1
not define
2
An event consisting of those elements which are not in A is called
primary event
derived event
simple event
complementry event
4
The idea of posteri probabilities was introduced by
Pascal
Peter and Paul
Thomas Bayes
M.loeve
3
The limiting relative frequency approach of probability is konwn is
statistical probability
classical probability
mathematical probability
all the above
1
2^k in factorials design menas K factors each at any
two levels
two treatment
two value
two parameters
1
If A and B be any two events which are independent then …
p(B/A)=P(B)
P(A/B)=P(A)
both 1 & 2
None of these
3
The events that are influenced by chance are called …. Events
impossible
random
certain
All the above
2
Values of continuous random variable are obtained by …..
counting
measurement
finite
countably infinite
2
The conditional probability P(Ei/A) is called….. Probability
priori
posteriori
hypotheses
conditional
2
Mean of uniform function is….
a-b/2
a+b/2
(b-a+1)^2 -1
2a+b
2
All values of the random variable have the …. Constant probability
different
same
non
None of these
2
The density function integrates to…..
0
unity
2
infinity
2
The expected value and variance of a….. Distrubuted r.v are both equal .
poisson
binominal
normal
gamma
1
Parameters of binomial distrubutions are….
n&p
p&q
n
p
1
The curve representing the normal distrubutions is called….
probability curve
curve
normal
None of these
3
Continious probability distrubutions is ….. Distrubutions
normal
binominal
poisson
gamma
1
The normal curve is …..
bell shaped
tringular
rectangular
circular
1
Sum of independent exponential random variable lamda result in ……
uniform random variable
binominal r.v
gamma r.v
normal r.v
3
Random mating is one of the requirement for …..
hardy-weinberg law
mendels law
law of segregation
law of dominance
1
In sex linkage, reciprocal cross yeild identical result
TRUE
FALSE
remain same
None of these
2
In a matrix of transition probability the element aij where i=j is …..
gain
loss
retention
None of these
3
In a matrix of transition probability the probability values should add up to one in each
row
column
diagonal
All the above
1
Specificity is also know as ……
true negative rate
true positive rate
true rate
false rate
1
For the chi-square test to be effective the expected values for each cell in the contingency table has to be at least
3
5
10
2
2
Contingency tables and degree of freedom are key elements of the chi-square test
true
FALSE
remain same
None of these
1
The only people who likely would be genetically identical are …… twins
monozygotic
dizygotic
both 1 & 2
None of these
1
What is the maximum percentage of recombination frequency between two genes?
75%
100%
50%
25%
2
The inheritance of new combinations of alleles in children result from ……
puberty
recombinations
genetic linkage
None of these
2
What is the unit of linkage map?
morgan
centimorgan
centimeter
angstrom
2
If the true means of the k populations are equal then MSTR/MSE should be
close to 1
close to -1
negative value betn 0&1
more than 1.00
1
The ……. Sum of squares measures the variable of the observed values around their respective treatment means
error
total
treatment
interaction
1
As variability due to chance decreses the value of F will
decrease
same
increase
can't tell
3
2k in factorial design means k factors each at any
two levels
two treatments
two values
two parameters
1
What do ANOVA calculate
chi-square
F- ratios
t- scores
R ratios
2
If in a block the numbers of units is less than the numbers of treatments S then the block is said to be
complete
incomplete
unit < treatment block
insufficient block
2
A BIBD is said to be symmetrical if no. of blocks =
no.of factors
no. of treatments
no. of levels
no. of degree of factor
2
The ….. Sum of squares measures the variability of sample treatments means around the overall mean
error
interactions
total
treatments
4
There are two types of designs systematic design and ……
random design
GLSD
spilt plot design
BIBD
1
If the MSE of an ANOVA for six treatment groups is known you can compute
pooled standard deviation
standard deviation of each treatment group
dfi
All the above
1
Random variables are classified into …..
discrete r.v
continious r.v
both 1 & 2
None of these
3
p.d.f means …..
probability density function
distribution function
probability function
only density function
1
Applications of Bayes theorem are……
valuating depression tests performance
probability theory to electrolytic reaction
predicting environmental damage
All the above
4
Trial of random experimental is called ….. If it has only two possible outcomes that is success or failures
poissontrial
normal trial
gamma
bernoulli trial
4
The variance of binomial distrubutions is ……
var(x)=np
var(x)=pq
var(x)=npq
None of these
3
The mean of binomial distrubutions is …..
E(x)=npq
E(x)=pq
E(x)=np
E(x)=2p
3
Applications of poisson distrubutions are
quality control
no. of succesfull sale
new job applicants either accepts offer or rejects
the count of bacteria per c.c in blood
4
Normal distrubutions is also know as …..
normal law
laplacian
gaussian
All the above
4
…….. Are parameters of normal distrubutions
mean
standard deviation
mean and standard deviation
None of these
3
The Hardy- weinberg principle also known as …..
hardy-weinberg equilibrium
model theorem
both 1 & 2
None of these
3
Genes of sex linked characters are located on
chromosome 18
chromosome13
chromosome14
sex chromosome
4
This condition is essential for a population to be in hardy- weignberg equilibrium
random mating
no mutations
large population
All the above
4
The expected value of discrete random variable is …….
E(X)=X1P1+X2P2+….+XnPn
E(X)=X1+X2+…..+Xn
E(X)=P1+P2+……+Pn
X1P1-X2P2-….-XnPn
1
Let E and F be events of a sample spaces S of an experiment then……
P(S/F)=P(F/F)=1
P(E'/F)=1-P(E/F)
both 1 & 2
only 1
3
If P(A)=7/13 , P(B)=9/13 and P(A∩B)=4/13 then P(A/B)=……
(4/9)
(9/4)
(13/7)
(13/4)
1
Roll a die and let X be the upward face showing what is a mean ?
(2/7)
(2/3)
(7/2)
(2/5)
3
Given X∼B(n,p) , if n=10 and p= 0.4 then Var(X) =……
4
2.2
2.4
2
3
The expected frequency of X=0 is F(0) =…..
N
N x p(0)
p(0)
N+p(0)
2
Parameter of poisson distribution is …..
λ
X
both 1 & 2
None of these
1
The expected value of an exponentially distributed r.v. X with rate parameter θ is …
1/θ^2
1/θ
θ
All the above
2
The variance of an exponentially distributed r.v. with rate parameter θ is
1/θ^2
1/θ
θ
None of these
1
Γ(1/2)=……
π
√π
√2π
2
If X ∼ Gamma(α,λ) then mean of X is …..
α/λ
λ/α
λ
α
1
Γ(n)=….
n!
(n-1)!
n(n-1)!
n
2
…….. Is a scientific field concerned with the development of statistical methods for drawing interference from genetic data
Statistics
statistical genetics
genetics
None of these
2
In Markov analysis we are concerned with the probability that the
state is part of system
system is in a particular state at given time
time has reached a steady state
transition will occur
2
……. Is a processs a model or rule to generate path sequence of random motion .
Random walk
steadt walk
Race walk
Brisk walk
1
Sensitivity is also called as …..
true negative rate
true positive rate
true rate
false rate
2
A random walk is mathematical object known as ..
Stochastic process
random process
both 1 & 2
None of these
3
The null hypothesis is the chi-square test states that …
The row & columns in the table are associated
The row & columns in the table are not associated
Neither of the two
None of these
2
The data obtained by enumeration or counting is called ….. data
measurement
enumeration
primary
secondary
2
Repulsion and coupling are two faces of…
mutation
chiasmata
linkage
crossing over
3
The tendency of two or more than two genes to stay together during inheritance is called ….
Genetics
Gene interaction
crossing over
linkage
4
Analysis of variance is a statistical method of comparing the several populations.
means
variances
standard deviation
None of these
1
When conducting on ANOVA ,FDATA will always fall within what range ?
between 0 & infinity
between 0 and 1
between - infinity and infinity
between 1 & infinity
1
The error deviations within the SSE statistic measures distances …
between groups
within groups
both 1 & 2
None of these
2
How many dependent variables must you have for an ANOVA to be conducted ?
only 1 continous variable
3 ratio variables
3 ordinal variables
3 interval variables
1
What must you include when reporting on ANOVA ?
means
p value
F statistic
All of these
4
In one -way ANOVA which of following is used within the F -ratio as a measurement of the variance of individual observations ?
SSE
MSE
MSTR
None of these
1
…… is model for a series of discrete events where the average time between events is known but the exact timing of events is random.
Normal process
poisson process
Binomial process
None of these
2
Poisson process is a type of markov process
TRUE
FALSE
Neither true nor false
None of these
1
Markov chain is aperiodic
FALSE
true positive rate
Neither true nor false
None of these
2
Genotype-enviroment interaction (GEI) plays a key role in developing strategies for ….
Crop improvement
soil improvement
neither of two
None of these
1
ANCOVA means
Analysis of variance
Analysis of covariance
Analysis of correalation
Analysis of regression
2
Poisson process meets the following criteria …
events are independent of each other
The average rate is constant
Two events cannot occur at the same time
All the above
4
….. Has great article on applying a poisson process to bus arrival times which better with made up data than real world data.
Jake vanderplas
john tukey
susan murphy
None of these
1
The Analysis of covariance is done by using …..
regression
linear regression
correlation
All the above
2
Markov chains have many applications as statistical models of real world process such as …
queues
currency exchange rates
animal populationdynamics
All the above
4
Markov processes are the basis for general stochastic simulation methods known as …
markov chain
markov chain monte carlo
only monte carlo
None of these
2
ANCOVA is general linear model which blends ….
ANOVA
regression
both 1 & 2
None of these
3
The ANOVA model assumes a linear relationship between the …
response
covariate
both 1 & 2
None of these
3
…… is a mathematical system that experiences transitions from one state to another according to certain probabilistic rules
markov chain
random model
random walks
None of these
1
Assumptions of ANCOVA is …..
linearity of regression
homogeneity of error variances
independence of error terms
All the above
4
Examples of poisson processs ..
customers calling a help center
visitors to website
radioactive decay in atoms
All the above
4
ANCOVA is similar to … ANOVA
factorial
nonfactorial
normal
None of these
1
The probability of sure event is
0
1
10
100
2
A table with all possible value of a random variable & it corressponding probabilities is called
Probability mass function
Probability density function
Probability distribution
cumulative distribution function
3
In the simultaneous tossing of two fair coins, the probability of having at least one head is
0.5
0.25
0.75
1
3
For two events A & B if P(A)=P(A/B)=0.25 & P(A/B)=0.5 then
A & B are mutually exclusive events
A & B are independent
A is subset of B
P(A'/B)‡ 0.75
2
A bin contain 4 red & 6 blue balls & three balls are drawn at random. Find probability such that both are of the same colour.
0.35
0.2
0.1
0.57
2
Which of the following statement is true?
P(A/B)≥P(A)
P(A/B)≤P(A)
P(A/B)=P(A)
Nothing can be said about the magnitude of P(A) & P(A/B)
4
If A & B are independent event with P(A)=0.4 & P(B) =0.25 then P(AUB) is equal to
0.65
0.55
0.1
Not enough information is given to answer this question
2
The expected value of a discrete random variable X is given by
∑P(x)
∑xP(x)
P(x)
1
2
Let X take value -1 , 0 , 1 & 2 with probabilities 0.2 , 0.4 , 0.1, & 0.3 respectively. Then X^2 take values 0 ,1, &4 respectively
0.4 , 0.3, 0.3
0.4, 0.2, 0.5
0.16, 0.02, 0.82
0.2, 0.4, 0.1, 0.3
1
Which of the following is not discrete random variable?
Number of students present in the cla
Number of person possessing O -ve blood group in a blood donatio
Number of daughters born to a couple until they g
Weight of a new born baby.
4
If X & Y denote the point obtained when two six face unbiased dice are thrown then P (X=Y) is
0.5
0.16
0.04
0.02
2
If X follows discrete uniform distribution on 0, 1, n & the mean of the distribution is 6. Hence the value of n is
6
18
36
12
4
Suppose X & Y are two independent discrete uniform random variables with parameters n. The distribution of X+Y is
discrete uniform with parameters 2n
discrete uniform random variables with parameters n^2.
Binomial Distribution
None of the above
4
How many outcomes can a bernoulli trial ?
3
2
5
2^n
2
A random variable has Binomial distribution with parameter n & p then
mean < variance
mean > variance
mean = variance
mean ≤ variance
2
If m is the mean of poission distribution the standard deviation is given by
√m
m^2
m
m/2
1
If X~ b(n1,p) & Y ~b(n2, p) & X & Y are independent then the distribution of X+Y
b(n1+n2 , 2p)
b(n1+n2 , p)
b(n1+n2 , q)
not a binomial distribution
2
If X1 & X2 are two independent poission variable with parameter m2 & m2 respectively then (X1 +X2) ~
P(m1 , m2)
P(m1 - m2)
P(m1 + m2)
None of these
3
Binomial distribution is
Continuous distribution
Discrete distribution
Irregular distribution
Not a Probability distribution
2
The recurrence relation between P(x) & P(x+1) in a poission distribution is given by
P(x+1) - mP(x)=0
mP(x+1) - P(x)=0
(x+1)P(x) - xP(x+1)=0
(x+1)P(x+1) - mP(x)=0
4