SES - Inferential Stats

Question 1

Left skewed?

Answer 1

Mean < Median.

Question 2

Right skewed?

Answer 2

Mean > Median.

Question 3

Descriptive stats?

Answer 3

Provide a concise summary of data either numerically or graphically.

Question 4

What does sampling involve?

Answer 4

Randomly selecting a subset of people from a population who are representative of it, perhaps in stratified groups e.g. gender, sport etc.

Question 5

Why do we use sampling? What is this known as?

Answer 5

To infer things about the population = Statistical inference.

Question 6

2 types of hypothesis? What do they state?

Answer 6

1. ) Experimental = States effect will be present.

2. ) Null = States effect will be absent.

Question 7

What does testing hypothesis involve?

Answer 7

Applying or building statistical models to or of the data collected.

Question 8

Why is mean a statistical model?

Answer 8

It is a hypothetical value and not one which is observed.

Question 9

What does null hypothesis significance testing involve?

Answer 9

Computing probabilities to evaluate evidence for the competing hypothesis.

Question 10

What does null hypothesis significance testing provide?

Answer 10

A rule-based framework for deciding whether to accept a hypothesis.

Question 11

How many decimal places is probablility reported to?

Answer 11

2

Question 12

4 basic principles of null hypothesis significance testing?

Answer 12

1. ) We assume the null hypothesis is correct.
2. ) We fit a statistical model to our data to determine how likely it is to get a result like this if the null hypothesis was correct.
3. ) To determine how well the model fits the data, the probability of that data fitting the model if the null hypothesis if correct is calculated.
4. ) If probability is small, we accept the experimental hypothesis.

Question 13

P value? What is it known as?

Answer 13

A probability number showing the strength of the evidence against the null hypothesis.
Known as alpha level.

Question 14

When do we accept the null hypothesis in relation to p-value?

Answer 14

2 data sets are compared and if they are not really different the p-value is closer to 1 and we accept the null hypothesis.

Question 15

When do we reject the null hypothesis in relation to p-value?

Answer 15

2 data sets are compared and if they are very different the p value is closer to 0 and we reject the null hypothesis.

Question 16

What is the standard across science when using p-values in relation to the null hypothesis? Example?

Answer 16

To use the p-values at or less than 0.05* to reject the null hypothesis and accept the experimental.
E.g. 95% due to systematic effect and 5% chance of error.

Question 17

What is the term ‘significant’ used to describe in relation to p-values?

Answer 17

To describe the differences/relationships for which a p-value under 0.05 is found.

Question 18

Type 1 error? What is it known as?

Answer 18

Occur where we erroneously find a p-value to reject the null hypothesis, when in fact we should accept it.
Known as a false positive.

Question 19

Type 2 error? What is it known as?

Answer 19

Occur where we erroneously find a p-value to accept the null hypothesis, when in fact we should reject it.
Known as a false negative.

Question 20

When do inflated error rates occur?

Answer 20

If we run multiple statistical tests on the same data set.