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Research Methods in Psychology. Data Analysis and Interpretation. Null Hypothesis Significance Testing (NHST).

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research methods in psychology

Research Methods in Psychology

Data Analysis and Interpretation

null hypothesis significance testing nhst
Null Hypothesis Significance Testing (NHST)
  • Null hypothesis testing is used to determine whether mean differences among groups in an experiment are greater than the differences that are expected simply because of error variation (chance).
null hypothesis significance testing nhst1
Null Hypothesis Significance Testing (NHST)
  • The first step in null hypothesis testing is to assume that the groups do not differ — that is, that the independent variable did not have an effect (i.e., the null hypothesis — H0).
  • Probability theory is used to estimate the likelihood of the experiment’s observed outcome, assuming the null hypothesis is true.
nhst continued
NHST (continued)
  • A statistically significant outcome is one that has a small likelihood of occurring if the null hypothesis is true.
    • We reject the null hypothesis, and conclude that the independent variable did have an effect on the dependent variable.
    • A statistically significant outcome indicates that the difference between means obtained in an experiment is larger than would be expected if error variation alone (i.e., chance) were responsible for the outcome.
nhst continued1
NHST (continued)
  • How small does the probability have to be in order to decide that a finding is statistically significant?
    • Consensus among members of the scientific community is that outcomes associated with probabilities of less than 5 times out of 100 (p < .05) if the null hypothesis were true are judged to be statistically significant.
    • This is called alpha (α) or the level of significance.
nhst continued2
NHST (continued)
  • What does a statistically significant outcome tell us?
    • An outcome with a probability just below .05 (and thus statistically significant) has about a 50/50 chance of being repeated in an exact replication of the experiment.
    • As the probability of the outcome of the experiment decreases (e.g., p = .025, p = .01, p = .005), the likelihood of observing a statistically significant outcome (p< .05) in an exact replication increases.
    • APA recommends reporting the exact probability of the outcome.
nhst continued3
NHST (continued)
  • What do we conclude when a finding is not statistically significant?
    • We do not reject the null hypothesis if there is no difference between groups.
    • However, we don’t necessarily accept the null hypothesis either — that is, we don’t conclude that the independent variable did not have an effect.
    • We cannot make a conclusion about the effect of the independent variable. Some factor in the experiment may have prevented us from observing an effect of the independent variable (e.g., too few participants).
nhst continued4
NHST (continued)
  • Because decisions about the outcome of an experiment are based on probabilities, Type I or Type II errors may occur.
nhst continued5
NHST (continued)
  • A Type I error occurs when the null hypothesis is rejected, but the null hypothesis is true.
    • That is, we claim that the independent variable is statistically significant (because we observed an outcome with p< .05) when there really is no effect of the independent variable.
    • The probability of a Type I error is alpha — or the level of significance (p = .05).
nhst continued6
NHST (continued)
  • A Type II error occurs when the null hypothesis is false, but it is not rejected.
    • That is, we claim that the independent variable is not statistically significant (because we observed an outcome with p > .05) when there really is an effect of the independent variable that our experiment missed.
nhst continued7
NHST (continued)
  • Because of the possibility of Type I and Type II errors, researchers are tentative in their claims. We use words such as “support for the hypothesis” or “consistent with the hypothesis” rather than stating that a hypothesis has been “proven.”
nhst comparing two means
NHST: Comparing Two Means
  • The appropriate inferential statistical test when comparing two means obtained from different groups of participants is a t -test for independent groups.
  • The appropriate test when comparing two means obtained from the same participants (or matched groups) is a repeated measures (within-subjects) t-test.
t test for paired samples
T-test for Paired Samples
  • Research Design
    • Analysis of Independent Variable using two conditions
      • Experimental
      • Control
    • Same group of subjects is used
    • Each subject receives the experimental and control
    • Subjects may be also be matched according to certain characteristics
t test for paired samples1
T-test for Paired Samples
  • Statistic is based on the difference between the scores of correlated subjects
  • Score compared to a difference of 0
    • Null hypothesis assumes no difference
    • Population mean is equal to 0
  • T critical obtained in same manner as t test for single samples
anova
ANOVA
  • Used to analyze data from experiments that use more than two groups or conditions
  • F is a ratio of two independent variance estimates
  • Since F is a ratio of variance estimates, it will never be negative
overview of anova technique
Overview of ANOVA technique
  • F test allows us to make one overall comparison that tells whether there is a significant difference between the means of the groups
introduction f distribution
Introduction F Distribution
  • F distribution
    • F distribution is positively skewed
    • Median F value equals one
    • F distribution is a family of curves based on the degrees of freedom (df)
overview of anova technique1
Overview of ANOVA technique
  • In the independent groups design, there are as many groups as there are levels of the independent variable
  • Hypothesis testing:
    • Nondirectional
    • H0 states that there is no difference between conditions
overview of anova technique2
Overview of ANOVA technique
  • ANOVA partitions total variability of data (SST) into the variability that exists within each group (SSW) and the variability between groups (SSB)
    • SS= Sum of Squares
overview of anova technique3
Overview of ANOVA technique
  • SSB andSSW are both used as independent estimates of the H0 population variance
  • F ratio
anova continued
ANOVA (continued)
  • The ANOVA Summary Table provides the information for estimating the sources of variance: between groups and within groups.

Source Sum of Squares (SS) df Variance Estimate F-test p

Between Groups 54.55 3 18.18 7.80 .002

Within Groups 37.20 16 2.33

  • The F-test is the Between group variance estimate is divided by the within group variance estimate(18.18 ÷ 2.33 = 7.80).
  • This F-test is statistically significant because .002 < .05.