Hypothesis Testing

ELESTA1

Hypothesis Testing

A systematic procedure for deciding whether the results of a research study, which examines a sample, support a particular theory or practical innovation, which applies to the population (Aron & Aron (2004).

An example of data for Hypothesis Testing

A researcher wanted to determine the relationship between a students performance in general psychology and his attitude towards the subject.

Performance was measured through a series of tests in GENPSYC

Attitude is measured through by the Shore and Shore’s Attitude Scale.

Steps in Hypothesis Testing

STEP 1: State the Null and alternative Hypothesis

H0=There is no significant relationship between attitude and performance. r=0

H1=There is a significant relationship between attitude and performance r=0

Steps in Hypothesis Testing STEP2: Determine the alpha level of

significance, degrees of freedom and critical value

Alpha level: α=.05, .01 5% or 1% of the comparison distribution in

which a sample would be considered an extreme that the possibility that it came from a distribution like this would be rejected.

5% or 1% = region of rejection 95 or 99%=region of acceptance

Steps in Hypothesis Testing

Degrees of Freedom (df) refers to power of a statistical test The more cases the higher the df, then the

more probability the sample will represent the population.

df=n-2

Steps in Hypothesis Testing

Critical value Cut-off sample score How extreme a sample score is needed to

draw a confident conclusion

Steps in Hypothesis Testing

STEP 3: Computation Formulas are used to determine the

obtained or computed value

Steps in Hypothesis Testing

STEP 4: Decision Rule Decide whether to reject or retain the null

hypothesis Reject the null hypothesis if the probability

of getting a result is less than 5%, p<.05 When a sample score is so extreme that

researchers reject the null hypothesis, the result is said to be statistically significant

Steps in Hypothesis Testing

p < .05/.01 = reject the H0, significant p > .05/.01 = retain the H0, not significant Obtained value > critical value = reject the

H0, significant Obtained value < critical value =retain the

H0, not significant

Example1. Ho: There is no significant relationship

between attitude and performanceH1; There is a significant relationship between attitude and performance

2. N = 157, α=.05, df=155, r critical=.1613. r computed = .11, p value=.194. Decision=since the r obtained which

is .11 is less the r critical (.161), the null hypothesis is not rejected. There is no significant relationship between attitude and performance in general psychology

Illustration

Z=2.03, r=.161

Z=1.38r=.11

2.5% region of rejection

95%2.5% region of rejection

Decision Errors

Type 1 error = if you reject the null hypothesis when in fact the null hypothesis is true

Type 2 = in reality the research hypothesis is true, but the result doesn’t come out extreme enough to reject the null hypothesis

Decision error

Real situation

H0 is true

Real situation

H1 is true

H1 is supported

Reject Ho

Type I error

α

Study inconclusive

Do not reject H0

Type II error

β