yarensky final exam part 1

7/30/2019 Yarensky Final Exam Part 1

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Kevin Fisher

May 15, 2013

Psychology Statistics – Yarensky 05

Final Exam: Short Answer

Final Exam: Part 1 – Short Answer Questions about the Supplementary Readings in the Packet

1. In regards to hypothesis testing logic, the hypothesis follows a process. First, the

independent variable has no effect is an “innocent” way of thinking and process a

hypothesis. The next step is to collect data and prove this hypothesis wrong, this is like

guilt . Then, the innocence is retained until the evidence against it seems unlikely beyond

a reasonable doubt. This is rejecting the original hypothesis of innocence , leaving no

alternative but the choice that the independent variable (IV) did affect the dependent

variable (DV). Having a null hypothesis is that innocence – that the IV does not have any

effect on the DV . Generally, we want to reject this null hypothesis. The alternative

hypothesis says our initial hypothesis is incorrect, that the IV did have an effect on the

DV – we generally want to support the alternative hypothesis. When we collect data,

these are sample statistics that will be compared to the population parameters. If a sample

mean is different from the population mean, this may be due to sampling error – we must

evaluate if the difference in data is due to the effect of the IV or just sampling error. We

ask ourselves by how much we’d expect the sample mean to differ from the population

mean if sampling error were the only error (no alternative elements).

The standard error of the mean works with the observed effect size (sample mean –

population mean) as the standard error provides us an estimate of the average amount of



sampling error. The statistical test takes a ratio of the actual (observed effect) size to the

expected effect size. This ratio is like a z-score, but now it is being used as a statistical

test and represents the computed value of z based on our actual data. The point of this

testing is to prove our initial innocence wrong, which only leaves the alternative

hypothesis to believe. If the null hypothesis is true, the observed effect size is only the

result of sampling error. If the null hypothesis can be rejected, the observed effect size is

mainly a measure of the IV. If the difference between the population mean and the

sample mean is large in respect to the experiment, then we can conclude that the

difference is not solely due to sampling error but due to some other element, our IV. Tomake this decision clearly, we set our significance level, which is a level of how

confident we need to be with our results. Generally, .05 is an appropriate criterion. Now,

the critical value of z table is used. If the absolute value of the test statistic is greater than

the critical value of z, the null hypothesis can be rejected.

There are some possible errors in interpretation of the statistical test results. A

false positive, which is a “Type I” error, means a result that indicates a given condition

has been fulfilled, when it actually has not been fulfilled. A false negative is where a test

result indicates that a condition failed, while it actually was successful . A “Type II” error

is when the null hypothesis fails to be rejected and the null hypothesis is actually false.

Of course, we want to have more power in rejecting the null hypothesis -- a few factors

that affect the power of our test include sample size, size of experimental effect,

variability in the data, and statistical analysis issues.



have three or more samples, then there is only one method to work by, and that is

analysis of variance (ANOVA). Some problems to avoid are as follows: a correlation

determines strength while regression makes predictions of one variable based on another

known variable. A single-sample t-test is needed in the case that the population standard

deviation is not known – if it is known, a single-sample z-test can be used.

4. ANOVA is used in real-life research involving three or more levels of an independent

variable. This is extremely common research in the psychology field. The null hypothesis

for an experiment with four experimental conditions is as follows: H 0 : µ1 = µ 2 = µ 3 = µ4.

The alternative hypothesis is H 1 = Not H 0. After the hypotheses are instated, like other tests, the data is collected, tested, and analyzed. In an ANOVA, the test statistic is the F-

ratio. It is similar to z-observed or t-observed in the other tests. It is a ratio between two

numbers, one of which represents the observed effect size, and the other of which

represents expected effect size. The null hypothesis is this ratio equals 1.0, or the

treatment effect is the same as the experimental error. This hypothesis is rejected if the F-

ratio is significantly large enough that the possibility of it equaling 1.0 is smaller than

some pre-assigned criteria such as 0.05. SS is the sum of squares, which is the numerator

of the standard deviation/variance equation. The degrees of freedom are similar to what

we are used to in an ANOVA, and MS is the mean square, which is equal to standard

deviation squared, also the same as variance. F, which we have already discussed, is the

final f-ratio. If the probability at the end is less than or equal to .05, we can reject the null

hypothesis. If probability is bigger than .05, we have to fail to reject the null hypothesis.

yarensky final exam part 1

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