Hypotheses

hypothesis

used within experiments as a proposed explanation made on a basis of limited evidence

types of hypothesis

null and alternative

types of alternative hypothesis

1. non-directional (two tailed)

2. directional (one tailed

null hypothesis

• predicts there is no relationship between the independent and dependent variables

• results are due to chance

null hypothesis (example)

'there will be NO SIGNIFICANT DIFFERENCE in (DV) between (IV1) and (IV2)

alternative hypothesis

states there is a relationship between two variables being studied (one has an effect on the other)

non-directional hypothesis (two-tailed)

• no idea what will happen so say we think something happen but we don't know what

• no previous research to suggest what will happen -> keep both outcomes

non-directional hypothesis example

'drinking speedup will have a significant effect on talkativeness when compared to not drinking speedup'

directional hypothesis (one-tailed)

• previous research (more specific)

• use words like increase/decrease, bigger/smaller, less/more

• depending on previous research we make have one or two hypotheses

directional hypothesis example

drinking speedup with SIGNIFICANTLY INCREASE (1) / DECREASE (2) talkativeness compared to not drinking speedup

extraneous variable (EV)

any variable, other than the independent variable, that may affect the dependent variable

two types of extraneous variables

participant and situational variables

participant variables

any individual differences that may impact the dependent variable

situational variables

any factors or characteristics related to the environment that can impact the dependent variable

participant variable examples

gender, age, personality, motivation, concentration

situational variable examples

weather, temperature, time of day, noise, instruction

confounding variables

• type of extraneous variable

• changes in the same way as the independent variable

• alternative explanation for results

what do confounding variables do?

they make it difficult to know if the independent variable or confounding variable is affecting the dependent variable

confounding variable example

if you collect data on sunburns and ice cream consumption, an increase in ice cream consumption is linked with a higher probability of getting sunburnt (confounding variable may be temperature)

demand characteristics

cue from the researcher that may be interpreted by participants as revealing a purpose of an investigation - may lead to participant changing behaviour within the research situation

demand characteristics examples

1. study setting

2. study procedure

3. tools and instruments

4. researchers interactions

5. title of study - on consent sheet

6. rumours about the study

participant reactivity

when the participant acts 'unnaturally' - if we are aware someone is watching us we will act differently to appear in a certain way

investigator effect

occurs when a researcher unintentionally or unconsciously influences the outcome of the work they're doing