statistical inference i: hypothesis testing; sample size

Statistical Inference I: Hypothesis testing; sample

size

Statistics Primer Statistical Inference Hypothesis testing P-values Type I error Type II error Statistical power Sample size calculations

What is a statistic? A statistic is any value that can

be calculated from the sample data.

Sample statistics are calculated to give us an idea about the larger population.

Examples of statistics: mean

The average cost of a gallon of gas in the US is $2.65. difference in means

The difference in the average gas price in Los Angeles ($2.96) compared with Des Moines, Iowa ($2.32) is 64 cents.

proportion 67% of high school students in the U.S. exercise

regularly difference in proportions

The difference in the proportion of Democrats who approve of Obama (83%) versus Republicans who do (14%) is 69%

What is a statistic? Sample statistics are estimates of

population parameters.

Sample (observation)

Make guesses about the whole population

Sample statistics estimate population parameters:

Truth (not observable)

Mean IQ of some population

of 100,000 people =100

Sample statistic: mean IQ of 5 subjects

1105

11512496105110

What is sampling variation?

Statistics vary from sample to sample due to random chance.

Example: A population of 100,000 people has an

average IQ of 100 (If you actually could measure them all!)

If you sample 5 random people from this population, what will you get?

Truth (not observable)

1305

9595180160120

Sampling Variation

Mean IQ=100

905

8892958590

1105

11512496105110

985

9510486105100

Sampling Variation and Sample Size Do you expect more or less

sampling variability in samples of 10 people?

Of 50 people? Of 1000 people? Of 100,000 people?

Most experiments are one-shot deals. So, how do we know if an observed effect from a single experiment is real or is just an artifact of sampling variability (chance variation)?

**Requires a priori knowledge about how sampling variability

works…

Question: Why have I made you learn about probability distributions and about how to calculate and manipulate expected value and variance?

Answer: Because they form the basis of describing the distribution of a sample statistic.

Sampling Distributions

Standard error Standard Error is a measure of sampling

variability. Standard error is the standard deviation of a

sample statistic. It’s a theoretical quantity! What would the distribution of my statistic be if I

could repeat my experiment many times (with fixed sample size)? How much chance variation is there?

Standard error decreases with increasing sample size and increases with increasing variability of the outcome (e.g., IQ).

Standard errors can be predicted by computer simulation or mathematical theory (formulas).

The formula for standard error is different for every type of statistic (e.g., mean, difference in means, odds ratio).

What is statistical inference? The field of statistics provides

guidance on how to make conclusions in the face of chance variation (sampling variability).

Example 1: Difference in proportions Research Question: Are

antidepressants a risk factor for suicide attempts in children and adolescents?

Example modified from: “Antidepressant Drug Therapy and Suicide in Severely Depressed Children and Adults ”; Olfson et al. Arch Gen Psychiatry.2006;63:865-872.

Example 1 Design: Case-control study Methods: Researchers used Medicaid

records to compare prescription histories between 263 children and teenagers (6-18 years) who had attempted suicide and 1241 controls who had never attempted suicide (all subjects suffered from depression).

Statistical question: Is a history of use of antidepressants more common among cases than controls?

Example 1 Statistical question: Is a history of use of

particular antidepressants more common among heart disease cases than controls?

What will we actually compare? Proportion of cases who used

antidepressants in the past vs. proportion of controls who did

No (%) of cases

(n=263)

No (%) of controls (n=1241)

Any antidepressant drug ever 120 (46%) 448 (36%)

46% 36%

Difference=10%

Results

What does a 10% difference mean?

Before we perform any formal statistical analysis on these data, we already have a lot of information.

Look at the basic numbers first; THEN consider statistical significance as a secondary guide.

Is the association statistically significant? This 10% difference could reflect a

true association or it could be a fluke in this particular sample.

The question: is 10% bigger or smaller than the expected sampling variability?

What is hypothesis testing? Statisticians try to answer this

question with a formal hypothesis test

Hypothesis testing

Null hypothesis: There is no association between antidepressant use and suicide attempts in the target population (= the difference is 0%)

Step 1: Assume the null hypothesis.

Hypothesis Testing

Step 2: Predict the sampling variability assuming the null hypothesis is true—math theory (formula):

The standard error of the difference in two proportions is:

Thus, we expect to see differences between the group as big as about 6.6% (2 standard errors) just by chance…

033.1241

)1504568

1(1504568

263

)1504568

1(1504568

)-1()1(

21

n

pp

n

pp

Hypothesis Testing

In computer simulation, you simulate taking repeated samples of the same size from the same population and observe the sampling variability.

I used computer simulation to take 1000 samples of 263 cases and 1241 controls assuming the null hypothesis is true (e.g., no difference in antidepressant use between the groups).

Step 2: Predict the sampling variability assuming the null hypothesis is true—computer simulation:

Computer Simulation Results

What is standard error?

Standard error: measure of variability of sample statistics

Standard error is about 3.3%

Hypothesis Testing

Step 3: Do an experiment

We observed a difference of 10% between cases and controls.

Hypothesis Testing

Step 4: Calculate a p-value

P-value=the probability of your data or something more extreme under the null hypothesis.

Hypothesis Testing

Step 4: Calculate a p-value—mathematical theory:

003.=p;0.3=033.

10.=Z

Observed difference between the groups.

Standard error.

Difference in proportions follows a normal distribution.

A Z-value of 3.0 corresponds to a p-value of .003.

When we ran this study 1000 times, we got 1 result as big or bigger than 10%.

The p-value from computer simulation…

We also got 2 results as small or smaller than –10%.

P-valueP-value


From our simulation, we estimate the p-value to be:

3/1000 or .003

Here we reject the null.

Alternative hypothesis: There is an association between antidepressant use and suicide in the target population.

Hypothesis Testing

Step 5: Reject or do not reject the null hypothesis.

What does a 10% difference mean? Is it “statistically significant”? YES Is it clinically significant? Is this a causal association?

What does a 10% difference mean? Is it “statistically significant”? YES Is it clinically significant? MAYBE Is this a causal association? MAYBE

Statistical significance does not necessarily imply clinical significance.

Statistical significance does not necessarily imply a cause-and-effect relationship.

What would a lack of statistical significance mean?

If this study had sampled only 50 cases and 50 controls, the sampling variability would have been much higher—as shown in this computer simulation…

Standard error is about 10%

50 cases and 50 controls.

Standard error is about 3.3% 263 cases and

1241 controls.

With only 50 cases and 50 controls…

Standard error is about 10%

If we ran this study 1000 times, we would expect to get values of 10% or higher 170 times (or 17% of the time).

Two-tailed p-value

Two-tailed p-value = 17%x2=34%

What does a 10% difference mean (50 cases/50 controls)? Is it “statistically significant”? NO Is it clinically significant? MAYBE Is this a causal association? MAYBE

No evidence of an effect Evidence of no effect.

Example 2: Difference in means

Example: Rosental, R. and Jacobson, L. (1966) Teachers’ expectancies: Determinates of pupils’ I.Q. gains. Psychological Reports, 19, 115-118.

The Experiment (note: exact numbers have been altered)

Grade 3 at Oak School were given an IQ test at the beginning of the academic year (n=90).

Classroom teachers were given a list of names of students in their classes who had supposedly scored in the top 20 percent; these students were identified as “academic bloomers” (n=18).

BUT: the children on the teachers lists had actually been randomly assigned to the list.

At the end of the year, the same I.Q. test was re-administered.

Example 2 Statistical question: Do students in the

treatment group have more improvement in IQ than students in the control group?

What will we actually compare? One-year change in IQ score in the

treatment group vs. one-year change in IQ score in the control group.

“Academic bloomers”

(n=18)Controls (n=72)

Change in IQ score: 12.2 (2.0) 8.2 (2.0)

Results:

12.2 points 8.2 points

Difference=4 points

The standard deviation of change scores was 2.0 in both groups. This affects statistical significance…

What does a 4-point difference mean?

Before we perform any formal statistical analysis on these data, we already have a lot of information.

Look at the basic numbers first; THEN consider statistical significance as a secondary guide.

Is the association statistically significant? This 4-point difference could reflect

a true effect or it could be a fluke. The question: is a 4-point

difference bigger or smaller than the expected sampling variability?

Hypothesis testing

Null hypothesis: There is no difference between “academic bloomers” and normal students (= the difference is 0%)

Step 1: Assume the null hypothesis.

Hypothesis Testing

Step 2: Predict the sampling variability assuming the null hypothesis is true—math theory:

52.072

4

18

4

2

2

1

2

n

s

n

s

The standard error of the difference in two means is:

We expect to see differences between the group as big as about 1.0 (2 standard errors) just by chance…

Hypothesis Testing

In computer simulation, you simulate taking repeated samples of the same size from the same population and observe the sampling variability.

I used computer simulation to take 1000 samples of 18 treated and 72 controls, assuming the null hypothesis (that the treatment doesn’t affect IQ).

Step 2: Predict the sampling variability assuming the null hypothesis is true—computer simulation:

Computer Simulation Results

What is the standard error?

Standard error: measure of variability of sample statistics

Standard error is about 0.52

Hypothesis Testing

Step 3: Do an experiment

We observed a difference of 4 between treated and controls.

Hypothesis Testing

Step 4: Calculate a p-value


Hypothesis Testing

p-value <.0001852.

488 t

Step 4: Calculate a p-value—mathematical theory:

Difference in means follows a T distribution (which is very similar to a normal except with very small samples).

Observed difference between the groups.

Standard error.

A T88-value of 8.0 corresponds to a p-value of <.0001

A t-curve with 88 df’s has slightly wider cut-off’s for 95% area (t=1.99) than a normal curve (Z=1.96)

If we ran this study 1000 times, we wouldn’t expect to get 1 result as big as a difference of 4 (under the null hypothesis).

Getting the P-value from computer simulation…


Here, p-value<.0001

P-valueP-value

Here we reject the null.

Alternative hypothesis: There is an association between being labeled as gifted and subsequent academic achievement.

Hypothesis Testing

Step 5: Reject or do not reject the null hypothesis.

What does a 4-point difference mean? Is it “statistically significant”? YES Is it clinically significant? Is this a causal association?

What does a 4-point difference mean? Is it “statistically significant”? YES Is it clinically significant? MAYBE Is this a causal association? MAYBE

Statistical significance does not necessarily imply clinical significance.

Statistical significance does not necessarily imply a cause-and-effect relationship.

What if our standard deviation had been higher? The standard deviation for change

scores in both treatment and control was 2.0. What if change scores had been much more variable—say a standard deviation of 10.0?

Standard error is 0.52 Std. dev in

change scores = 2.0

Std. dev in change scores = 10.0

Standard error is 2.58

With a std. dev. of 10.0…

Standard error is 2.58

If we ran this study 1000 times, we would expect to get +4.0 or –4.0 12% of the time.

P-value=.12

What would a 4.0 difference mean (std. dev=10)? Is it “statistically significant”? NO Is it clinically significant? MAYBE Is this a causal association? MAYBE

No evidence of an effect Evidence of no effect.

Hypothesis testing summary Null hypothesis: the hypothesis of no effect

(usually the opposite of what you hope to prove). The straw man you are trying to shoot down.

Example: antidepressants have no effect on suicide risk P-value: the probability of your observed data if

the null hypothesis is true. Example: The probability that the study would have found

10% higher suicide attempts in the antidepressant group (compared with control) if antidepressants had no effect (i.e., just by chance).

If the p-value is low enough (i.e., if our data are very unlikely given the null hypothesis), this is evidence that the null hypothesis is wrong.

If p-value is low enough (typically <.05), we reject the null hypothesis and conclude that antidepressants do have an effect.

Summary: The Underlying Logic of hypothesis tests…

Follows this logic:

Assume A.

If A, then B.

Not B.

Therefore, Not A.

But throw in a bit of uncertainty…If A, then probably B…

Error and power Type I error rate (or significance level): the

probability of finding an effect that isn’t real (false positive).

If we require p-value<.05 for statistical significance, this means that 1/20 times we will find a positive result just by chance.

Type II error rate: the probability of missing an effect (false negative).

Statistical power: the probability of finding an effect if it is there (the probability of not making a type II error).

When we design studies, we typically aim for a power of 80% (allowing a false negative rate, or type II error rate, of 20%).

Type I and Type II Error in a box

Your Statistical Decision

True state of null hypothesis

H0 True (e.g., antidepressants do not increase suicide risk)

H0 False(e.g., antidepressants do

increase suicide risk)

Reject H0

(e.g., conclude antidepressants increase suicide risk)

Type I error (α) Correct

Do not reject H0(e.g., conclude there is insufficient evidence that antidepresssants increase suicide risk)

Correct Type II Error (β)

Reminds me of…

Pascal’s Wager

Your DecisionThe TRUTH

God Exists God Doesn’t Exist

Reject GodBIG MISTAKE Correct

Accept God Correct—Big Pay Off

MINOR MISTAKE

Type I and Type II Error in a box

Your Statistical Decision

True state of null hypothesis

H0 True H0 False

Reject H0 Type I error (α) Correct

Do not reject H0

Correct Type II Error (β)

Review Question 1If we have a p-value of 0.03 and so decide that our effect is statistically significant, what is the probability that we’re wrong (i.e., that the hypothesis test gave us a false positive)?

a. .03b. .06c. Cannot telld. 1.96e. 95%

Review Question 2Standard error is:

a. For a given variable, its standard deviation divided by the square root of n.

b. A measure of the variability of a sample statistic.

c. The inverse of sample size. d. A measure of the variability of a

characteristic.e. All of the above.

Review Question 3A randomized trial of two treatments for depression failed to show a statistically significant difference in improvement from depressive symptoms (p-value =.50). It follows that:

a. The treatments are equally effective.b. Neither treatment is effective.c. The study lacked sufficient power to detect a

difference.d. The null hypothesis should be rejected.e. There is not enough evidence to reject the null

hypothesis.

Review Question 4Following the introduction of a new treatment regime in a rehab facility, alcoholism “cure” rates increased. The proportion of successful outcomes in the two years following the change was significantly higher than in the preceding two years (p-value: <.005). It follows that:

a. The improvement in treatment outcome is clinically important.b. The new regime cannot be worse than the old treatment.c. Assuming that there are no biases in the study method, the new

treatment should be recommended in preference to the old. d. All of the above.e. None of the above.

Statistical Power Statistical power is the probability

of finding an effect if it’s real.

Can we quantify how much power we have for given sample sizes?

Null Distribution: difference=0.

Clinically relevant alternative: difference=10%.

Rejection region. Any value >= 6.5 (0+3.3*1.96)

study 1: 263 cases, 1241 controls

For 5% significance level, one-tail area=2.5%

(Z/2 = 1.96)

Power= chance of being in the rejection region if the alternative is true=area to the right of this line (in yellow)

Power here = >80%

Rejection region. Any value >= 6.5 (0+3.3*1.96)

Power= chance of being in the rejection region if the alternative is true=area to the right of this line (in yellow)


Critical value= 0+10*1.96=20

Power closer to 20% now.

2.5% area

Z/2=1.96


Critical value= 0+0.52*1.96 = 1

Power is nearly 100%!

Study 2: 18 treated, 72 controls, STD DEV = 2

Clinically relevant alternative: difference=4 points


Power is about 40%

Study 2: 18 treated, 72 controls, STD DEV=10


Power is about 50%

Study 2: 18 treated, 72 controls, effect size=1.0

Clinically relevant alternative: difference=1 point

Factors Affecting Power

1. Size of the effect2. Standard deviation of the

characteristic3. Bigger sample size 4. Significance level desired

average weight from samples of 100

Null

Clinically relevant alternative

1. Bigger difference from the null mean


2. Bigger standard deviation


3. Bigger Sample Size


4. Higher significance level

Rejection region.

Sample size calculations Based on these elements, you can

write a formal mathematical equation that relates power, sample size, effect size, standard deviation, and significance level…

Simple formula for difference in proportions

221

2/2

)(p

)Z)(1)((2

p

Zppn

Sample size in each group (assumes equal sized groups)

Represents the desired power (typically .84 for 80% power).

Represents the desired level of statistical significance (typically 1.96).

A measure of variability (similar to standard deviation)

Effect Size (the difference in proportions)

Simple formula for difference in means

Sample size in each group (assumes equal sized groups)

Represents the desired power (typically .84 for 80% power).

Represents the desired level of statistical significance (typically 1.96).

Standard deviation of the outcome variable

Effect Size (the difference in means)

2

2/2

2

difference

)Z(2

Zn

Sample size calculators on the web… http://biostat.mc.vanderbilt.edu/twi

ki/bin/view/Main/PowerSampleSize

http://calculators.stat.ucla.edu http://

hedwig.mgh.harvard.edu/sample_size/size.html

These sample size calculations are idealized

•They do not account for losses-to-follow up (prospective studies)

•They do not account for non-compliance (for intervention trial or RCT)

•They assume that individuals are independent observations (not true in clustered designs)

•Consult a statistician!

Review Question 5Which of the following elements does not

increase statistical power?

a. Increased sample sizeb. Measuring the outcome variable more

preciselyc. A significance level of .01 rather

than .05d. A larger effect size.

Review Question 6Most sample size calculators ask you to input a value for . What are they asking for?

a. The standard errorb. The standard deviationc. The standard error of the differenced. The coefficient of deviatione. The variance

Review Question 7For your RCT, you want 80% power to detect

a reduction of 10 points or more in the treatment group relative to placebo. What is 10 in your sample size formula?

a. Standard deviationb. mean changec. Effect sized. Standard errore. Significance level

Homework Problem Set 3 Reading: continue reading

textbook Reading: p-value article Journal article/article review sheet

statistical inference i: hypothesis testing; sample size

Documents

population sample statistics

sample observation

sample data

sampling distributions

increasing sample size

fixed sample size

standard error standard

observable sampling