Week 2 Hypothesis Structuring (Textbooks) Flashcards

To supplement our understanding of hypothesis structuring covered in the first part of lecture 2

1
Q

How does Andy Field define a hypothesis?

A

*A hypothesis is a prediction about the state of the world

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

How does Andy Field define the Experimental Hypothesis?

A
  • a synonym for the alternative hypothesis
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

How does Andy Field define the Alternative Hypothesis?

A
  • The prediction that there will be an effect
  • i.e. that my experimental manipulation will have some effect or
  • that certain variables will be related to each other
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

How does Andy Field define the Null Hypothesis?

A
  • the reverse of the experimental hypothesis

* that my prediction is wrong and the predicted effect does not exist

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Why do we need a null hypothesis?

A
  • we need a null hypothesis because we cannot prove the experimental hypothesis using statistics, but we can reject the null hypothesis
  • If our data gives us confidence to reject the null hypothesis, then this provides support for our experimental hypothesis
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

What does Andy Field tell me about directional hypothesis?

A
  • Hypothesis can be directional or non-directional

* A directional hypothesis states that an effect will occur, but it also states the direct of the effect

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

What do inferential statistics tell me according to Andy Field?

A
  • Inferential statistics helps to confirm or reject my predictions,
  • telling me whether the experimental hypothesis is likely to be true
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Why does Fisher recommend 95% as a useful confidence threshold?

A
  • only when I am 95% certain that a result is genuine (i.e. not chance) should I accept it to be true
  • therefore, if there is only a 5% probability (.05) of something occurring by chance, then we can accept this as a genuine effect
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

How does including effect sizes with significance levels of 0.05 help interpretation of results?

A

The use of effect sizes strikes a balance between using arbitrary cut-offs (.01, .05) for significance and assessing whether an effect is meaningful within the research context

How well did you know this?
1
Not at all
2
3
4
5
Perfectly