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PSYC 241
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Kristen Leighton
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Lecture 8

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University of North Dakota

Psychology

PSYC 241

Kristen Leighton

Spring

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Lesson 8: Introduction to Hypothesis Testing
● Hypothesis Testing
○ Goal: rule out chance (sampling error) as a plausible explanation for results
○ Hypothesis testing helps determine whether a treatment has an effect
○ To evaluate research in which:
■ 1. A sample is selected from the population
■ 2. The treatment is administered to the sample
■ 3. The people in the sample are measured after treatment
○ If people in sample are noticeably different from those in original population, we have
evidence of treatment effect
○ It is also possible, however, that the difference between sample and population is due
to sampling error
○ A statistical hypothesis is an assumption about a population parameter
○ Best way to determine whether hypothesis is true would be to examine entire
population
■ Impractical --> impossible
○ Random sample is proxy for population
○ Use this sample to test hypothesis
○ Two possible explanations:
■ 1. The difference between sample and population can be explained by sampling
error
■ No treatment effect
■ 2. The difference is too large to be due to sampling error
■ Treatment effect
● Cannot "prove" things to be true
● Much easier to show that something is false
● Dilemma for researchers:
○ We want to support our hypotheses, but the techniques available are better for showing
that something is false
● Null Hypothesis
○ Two types of statistical hypotheses:
■ 1. Null hypothesis: No difference
■ H0
■ No difference between sample statistics and population parameters or
■ No difference between samples
--> no difference between populations
● Any minor differences due to chance
● Alternative Hypothesis
○ 2. Alternative hypothesis: Difference
■ H1or Ha
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■ Difference between sample statistics and population parameter or
■ Difference between two samples
--> difference between populations
● Difference due to some non-random cause
● Even though we are using samples to test our hypothesis, the hypotheses are always stated
based on populations
● Statistical Notation
○ H 0 µ0= µ1
■ No difference in population means
○ H : µ doesn't equal µ
1 0 1
○ H a µ0doesn't equal1µ
■ Population means are not equal (two-tailed, non-directional)
■ Non-directional tests are more stringent; more conservative
● Directional Tests
○ If researchers predicts a specific direction for the treatment effect (increase or
decrease), can incorporate the directional prediction into the hypothesis test
○ The result is called a directional test or a one-tail test
○ Directional test includes directional prediction in statement of hypotheses and location
of the critical region
○ For example, if the original population has a mean of 80 and treatment is predicted to
increase the scores, the null hypothesis would state that after treatment:
■ H0: mean is equal to or < 80 (there is no increase)
● Statistical Notation
○ H : µ < µ
1 0 1
○ H 1 µ0> µ1
○ H a µ0< µ1
○ H a µ0> µ1
■ One mean is greater (or less than) the other (one-tailed, directional)
● The Null hypothesis, the Alpha Level, the Critical Region, and the Test Statistic (can be written on
calculation sheet)
○ 5 steps in hypothesis testing:
■ Step 1: State Hypothesis
■ State the hypotheses; select an α (alpha) level
■ Null hypothesis0 H , always states that the treatment has no effect
■ Alpha level establishes a criterion, or "cut-off," for making a decision
about the null hypothesis
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■ Alpha level also determines risk of Type I error
■ Step 2: Set Criteria
■ Locate the critical region
■ The critical region consists of outcomes that are very unlikely to occur if
the null hypothesis is true
■ Means that are almost impossible to obtain if the treatment has
no effect
■ "almost impossible" means probability (p) that is less than the
alpha level
■ Step 3: Collect and analyze data

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