Question

QUESTION 1

The fundalmental condition that permits proper statistical inference is

	a.	having a large sample
	b.	normal distribution of scores
	c.	random sampling
	d.	knowledge of the values of the parameters of the population

QUESTION 2

Randomization and random sampling

	a.	can be substituted for each other
	b.	often amount to the same thing
	c.	are different procedures
	d.	are synonymous

QUESTION 3

Randomization is used to

	a.	assigning participants to experimental conditions
	b.	to analyze data from random samples.
	c.	as a less complex substitute for random sampling
	d.	to select subjects randomly from a population

QUESTION 4

A population characteristic is known as a(n)

	a.	parameter
	b.	basic value
	c.	element
	d.	statistic

QUESTION 5

"statistic" is to "parameter" as

	a.	"calculated" is to "given"
	b.	"random sampling" is to "randomization"
	c.	"sample" is to "populaton"
	d.	"mean" is to "standard deviation"

QUESTION 6

Whether or not a sample is considered random depends on

	a.	the method of selection
	b.	how closely it resembles the population
	c.	the size of the sample
	d.	None of the above

QUESTION 7

Each score in a random sampling distribution of means represents

	a.	a random data point
	b.	a single individual
	c.	a standard score
	d.	a sample mean

QUESTION 8

Which of the following is a parameter?

	a.	σ
	b.	Xbar
	c.	r
	d.	s

QUESTION 9

The standard error of the mean

	a.	is a standard deviation
	b.	is the average amount by which sample values are in error
	c.	is given in terms of standard units
	d.	is larger for larger populations

QUESTION 10

A sampling distribution is a distribution of

	a.	values of a statistic obtained from samples
	b.	scores obtained from samples
	c.	values of a parameter obtained from samples
	d.	any of the above

Answer 1

1) The fundamental condition that permits proper statistical inference is c) RANDOM SAMPLING.

Because Statistical inference comprises the application of methods to analyze the sample data in order to estimate the population parameters. The basic assumption in statistical inference is that each individual within the population of interest has the same probability of being included in a specific sample. The use of randomization in sampling allows for the analysis of results using the methods of statistical inference. Statistical inference is used to infer conclusions about a given population based on results observed through random sampling.

2) Randomization and random sampling​ ​c) ARE DIFFERENT PROCEDURES.

Random Samples and Randomization are two different things, but they have something in common as the presence of random in both names suggests — both involve the use of a probability. With random samples, chance determines who will be in the sample. With randomization [ random assignment], chance determines the assignment of treatments.

A random sample is drawn from a population by using a probability device. Whereas randomization refers to the use of a probability device to assign subjects to treatment.

3) Randomization is used to​ a) ASSIGNING PARTICIPANTS TO EXPERIMENTAL CONDITIONS.

Randomization in an experiment is where you choose your experimental participants randomly. In otehr words, it is a method based on chance alone by which study participants are assigned to a treatment group or experimental conditions.

4) A population characteristic is known as a​) PARAMETER.

A measurable characteristic of a population, such as a mean or standard deviation, is called a parameter.

​5) "statistic" is to "parameter" as​ c) "SAMPLE" is to "POPULATION".

​​Since sample characteristic is called statistic and population characteristic is called parameter.

6) Whether or not a sample is considered random depends on​ b) HOW CLOSELY IT RESEMBLES THE POPULATION.

Probability sampling is also referred to as random sampling or representative sampling. When random sampling is used, each element in the population has an equal chance of being selected as sample points (simple random sampling) or a known probability of being selected (stratified random sampling). The sample is referred to as representative because the characteristics of a properly drawn sample represent the parent population in all ways.

7) Each score in a random sampling distribution of means represents​ d) SAMPLE MEAN.

A sampling distribution of means is how the sample means are distributed. In other words, if you were to draw large samples from a population repeatedly, and graph the resulting sample means, the sampling distribution is what that graph would look like.

8) Which of the following is a parameter? a)

​9) The standard error of the mean​ a) IS A STANDARD DEVIATION.

The standard error (SE) of a statistic (usually an estimate of a parameter) is the standard deviation of its sampling distribution or an estimate of that standard deviation.

The standard error of the mean, also called the standard deviation of the mean, is a method used to estimate the standard deviation of a sampling distribution.

10) A sampling distribution is a distribution of a) VALUES OF STATISTIC OBTAINED FROM SAMPLES.

​​A sampling distribution is the probability distribution of a statistic obtained through a large number of samples drawn from specific population.