Statistics

Question 1

What is a Sample Statistic?

Answer 1

Is a quantity that describes some characteristic of a sample with respect to a specific variable.

Question 2

Pro and Con of Median

Answer 2

Pro: Is insensitive to extreme scores in the data set

Con: Doesn’t reflect the shape of the scores – i.e. doesn’t care how far away extreme scores are

Question 3

Pro and Con of Mode

Answer 3

Pro: Easy to calculate from a histogram and easy to understand – the most common value.

Con: Data set might have more than 1 mode or no mode at all

Question 4

2 Types of Non-Normally Distributed Data

Answer 4

Skewed

Bimodal

Question 5

Define Conditional Probability

Answer 5

Probability of an event given that something else is known/assumed, i.e. when given/assuming some other additional information.

Question 6

What does the z-score measure?

Answer 6

z measures how far away your sample is from the population mean in multiples of the standard deviation (how many standard deviations away is your sample from the mean)

Question 7

Define sampling Error.

Answer 7

The error associated with examining statistics calculated from a sample rather than the population

Occurs because in our sample we do not have all the members of the population

Pop. Parameters and sample statistics differ due to sampling error.

Question 8

How does sample size effect magnitude of sample error?

Answer 8

BIGGER SAMPLE = BIG SAMPLING ERROR LESS LIKELY

SMALLER SAMPLE = BIG SAMPLING ERROR MORE LIKELY

Question 9

Define Sampling Distribution.

Answer 9

A distribution of a sample statistic (e.g. mean, s.d., median, etc…) obtained by repeatedly sampling from a population.

Tells us important information about how a statistic changes from sample to sample

Question 10

Define Sampling Distribution of the Mean (SDM)

Answer 10

The sampling distribution of the mean describes a distribution ofsample means derived from samples of size N from a parent population.
The standard deviation of this distribution is commonly referred to as the standard error of the mean or standard error for short.

Question 11

Define the Central Limit Theorem.

Answer 11

The sampling distribution of the mean approaches a normal distribution, as the sample size increases.

As a sample size increases, the sample mean and standard deviation will be closer in value to the population mean μ and standard deviation σ.

A sufficiently large sample can predict the parameters of a population such as the mean and standard deviation.

Question 12

What is a Confidence interval?

Answer 12

A confidence interval (CI) describes an interval (i.e. a range) of values for our population parameter, together with a specified level of confidence that the parameter is in that range

It is simply a way to measure how well your sample represents the population you are studying.

Question 13

What is Type 1 Error

Answer 13

When we reject H0 when it is in fact true. (H1 is False)

Type 1 errors will occur naturally (i.e. just due to random sampling error) with probability p = a (i.e. 0.05)

Question 14

What is Type 2 Error?

Answer 14

We Fail to reject H0 when it is in fact incorrect. (H1 is True)

Question 15

Why does Type 1 Error Occur?

Answer 15

Type I errors occur because even if your p-value is small there is still a (small) chance that your data was unusually extreme (and so you rejected the NULL) just due to sampling error.

Question 16

Why does Type 2 Error Occur?

Answer 16

Type II errors often arise because of a problem with your study:

- Perhaps your sample was biased 
- Perhaps there was an error in your experimental task 
- Perhaps your sample size was too small

Question 17

What is Continous Variable?

Answer 17

Continuous variables can take on absolutely any value within a given range.

Question 18

What is a discrete Variable?

Answer 18

Discrete variables can only take on certain discrete values in a range.

Question 19

What is a Categorical Variable?

Answer 19

Categorical variables are those in which we simply allocate people to categories.