Session 2a Flashcards
what is a Hypothesis
Assumption/prediction made about a population parameter (NOT about a sample estimate)
Ad campaign A is preferred to B
Getting $1 million will make people happier 6 months later
Drug A will increase survival rate of AIDS patients
these are example of what
hypotheses
Traditionally, experimental research engages in a procedure for what
hypothesis testing (NHST)
what is (NHST)
Null hypothesis signifiance testing
is NHST used still
NHST is still widely used
More recent approaches focus on effect sizes and formation of confidence intervals
what are the Steps for Hypothesis Testing
Step 1: Set Up a hypothesis
Step 2: Choose α (significance level)1
Step 3: Examine your data and compute the appropriate test statistic
Step 4: Make the decision whether to “reject” or “not reject” the null hypothesis
Alternatively, look at the signifiance level (p-value) for the test statistic value
explain Step 1: Set Up a hypothesis
Usually a prediction that there is an effect of certain variable(s) in the
population Example:
Eating fries will give you high cholesterol
Null and Alternative Hypothesis
what is null hypothesis
(H0)
This is what we test statistically
No effect (“People will have equal cholesterol regardless of how many fries they eat”)
what is Alternative Hypothesis
(H1)
Research/experimental hypothesis
Some effect (“People eating more fries will have higher cholesterol than those who eat less fries”)
Sometimes Ha is used to denote the alternative hypothesis
what is Step 2: Choose α (alpha) (significance level)1
Decide the area consisting of extreme scores which are unlikely to occur if the null hypothesis is true
Proportion of times we are willing to accidentally reject H0, even if H0 is true
Conventionally, α = what
.05 or α = .01
The cutoff sample score for α is called what
the critical value
explain Step 4: Make the decision whether to “reject” or “not reject” the null hypothesis
Compare the calculated value of your test statistic to the critical value for α
in step 4, If your value is greater than or equal to the critical value what happens
reject H0. Otherwise, retain H0
a decision to reject H0 implies what
acceptance of H1
explain Alternatively, look at the signifiance level (p-value) for the test statistic value:
Such values often given by SPSS, R, or other statistical software If p ≤ α (e.g., p ≤ .05), what do you do to the null
reject H0. Otherwise, retain H0
this is a yes/no decision
If H0 is rejected, you may conclude what
that there is a statistically significant effect in the population
“Eating fries has a statistically significant effect on cholesterol levels”
“statistically significant” effect does not indicate what
We have a precise estimate of the effect
The effect is important or meaningful
explain how ‘stat sig’ does not indicate that We have a precise estimate of the effect
It may be that an effect is “significant”, but there is some error around our estimate
The amount of error is represented in the standard error for the estimate The effect may be smaller or larger than our estimate
explain how ‘stat sig’ does not indicate The effect is important or meaningful
Suppose we find that eating 1kg fries/month leads to 10g weight gain Is 10g really a meaningful amount?
Weight gain may be “significant” if it was observed from many people
what is a Confidence Interval
gives us information about the precision of our estimates
Example: a 95% confidence interval (CI) may indicate that true weight gain in the population is between 2g and 18g per month
We don’t know for sure that a 95% CI will contain the true value of the effect in the population
If we repeated our experiment many times, 95% of the time a 95% CI will contain the true effect
for Confidence intervals, Usually, we form {(1 − α) × 100}% CIs meaning what
If α = .05, we form a 95% CI
If α = .01, we form a 99% CI
for Confidence intervals, As sample size increases what happens to your estimate
our estimate becomes more precise
And our CI intervals may become smaller or more narrow
As α decreases what happens to the CI
Our CI intervals become larger or wider
