# PSYC 210 Study Guide - Midterm Guide: Null Hypothesis, Statistical Hypothesis Testing, Standard Score

by OC10032

School

Simon Fraser UniversityDepartment

PsychologyCourse Code

PSYC 210Professor

Cathy Mc FarlandStudy Guide

MidtermThis

**preview**shows pages 1-2. to view the full**6 pages of the document.**Chapter 4 – Intro to Hypothesis Testing 1

Hypothesis testing – procedure for deciding whether the outcome of a study (results for a sample) supports a

particular theory or practical innovation (which is thought to apply to a population)

Hypothesis – prediction, often based on informal observation, previous research, or theory, that is tested

in a research study

o Research hypothesis – statement in hypothesis testing about the predicted relation between

populations (often a prediction of a difference between population means); proposes a

relationship about two variables

o Null hypothesis – statement about a relation between populations that is the opposite of the

research hypothesis; statement that in the population there is no difference (or a difference

opposite to that predicted) between populations; a hypothesis that proposes no relationship or

difference between two variables

THE RESEARCH HYPOTHESIS AND NULL HYPOTHESIS ARE COMPLETE OPPOSITES. IF ONE IS TRUE, THE OTHER CANNOT BE. RESEARCH

HYPOTHESIS IS ALSO KNOWN AS THE ALTERNATE HYPOTHESIS (THE ALTERNATIVE TO THE NULL HYPOTHESIS), EVEN THOUGH THE RESEARCH

HYPOTHESIS IS WHAT WE ARE CONCERNED ABOUT.

Theory – set of principles that attempt to explain one or more facts/relationships/events; psychologists often

derive specific predictions from theories that are then tested in research studies

Population mean µ Population SD δ Null hypothesis H0

Sample mean M Sample SD SD Alternate hypothesis H1

Hypothesis-Testing Process:

Step 1: Restate the question as a

research hypothesis and a null

hypothesis about the populations.

The researchers are interested in

the effects on babies in general. The

purpose of studying samples is to know

about populations. That is why it’s useful

to restate the hypothesis in terms of a

population.

Population 1 = babies who receive the experimental treatment (vitamin) = experimental group

Population 1: Babies who take the specially purified vitamin.

Population 2 = comparison baseline of what is already known about babies in general = control group

Population 2: Babies in general (i.e., babies who don’t take the vitamin).

Prediction A: Population 1 babies will on the average walk earlier than Population 2 babies. Population 1 mean is

lower (babies receiving the special vitamin walk earlier) than the mean of Population 2. (µ1 < µ2)

The difference between populations = research hypothesis

Prediction B: Population 1 babies will on average not walk earlier than Population 2 babies. There is no age

difference at which Population 1 and 2 babies start walking; they start at the same time, on average (µ1 = µ2)

Only pages 1-2 are available for preview. Some parts have been intentionally blurred.

Chapter 4 – Intro to Hypothesis Testing 2

The populations aren’t different in the way predicted; opposite of the research hypothesis; a lack of

difference between populations = null hypothesis

Step 2: Determine the characteristics of the comparison distribution.

Hypothesis testing involves figuring out the probability of getting a particular result if H0 is true.

Babies in Population 2 (babies in the general population) have µ = 14 and δ=3. If H0 is true, Population 1 =

Population 2. This means that both populations have µ = 14, δ = 3, and follow normal curves.

In the hypothesis-testing process, you need to find the probability that you could have gotten a sample score as

extreme as what you got (i.e., a baby walking very early) if your sample was from a population with a distribution if

the H0 were true.

Comparison distribution – distribution used in hypothesis testing that represents the population situation

if the H0 is true; the distribution to which you compare the score based on your sample’s results.

Step 3: Determine the cutoff sample score on the comparison distribution at which the null

hypothesis should be rejected.

Before conducting, you should set a target against which you will compare your result: how extreme the sample

score would have to be for it to be too unlikely that they could get such an extreme score if the H0 were true

The cutoff sample score – point in hypothesis testing, on the comparison distribution at which, if reached

or exceeded by the sample score, you reject the null hypothesis (aka critical value)

Researchers generally reject the H0 if the probability of getting a sample this extreme (if the H0 were true) is less

than 5% (p < .05); some use a cutoff of 1% (p < .01).

Conventional levels of significance (p < .05, p < .01) – widely used significance levels

When a sample score is so extreme that researchers reject the H0 (conclusion = results concur with H1),

the result is said to be statistically significant

Step 4: Determine your sample’s score on the comparison distribution

Carry out the study and get the results for your sample. Calculate the Z score for the sample’s raw score based on

the µ and δ of the comparison distribution. Going back to the baby walking example...

The baby who was given the specially purified vitamin started walking at 6 months.

Of the comparison distribution to which we are comparing the results µ=14 and δ=3

A baby who walks at 6 months is 8 months below the population mean.

o 2⅔ standard deviations below the µ

o Z score for this sample baby on the CD is -2.67 [Z = (6 – 14)/3 = -2.67].

Step 5: Decide whether to reject the null hypothesis.

Compare your actual sample’s Z score (Step 4) to the cutoff Z score (Step 3).

In the example, the actual result was -2.67. Suppose the researchers decided that they would reject the

H0 if the sample’s Z score < -2. Since -2.67 is below -2, the researchers would reject the H0.

If the researchers reject the null hypothesis, the research/alternate hypothesis remains.

o In this example, the research team would conclude that the results of their study support the

research hypothesis that babies who take the specially purified vitamin walk earlier than babies

in general.

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