Psych Research Methods II Exam I

Hypothesis testing

The process of determining whether data support your prediction.

Null hypothesis

The hypothesis stating that there is no effect or difference.

Alternative hypothesis

The hypothesis stating that there is an effect or difference.

T-test

A statistical test used to compare the means of two groups when population variance is not known

Chi-squared test

A statistical test used to determine if there is a significant association between categorical variables.

ANOVA

Analysis of variance; a statistical method used to compare means among three or more groups.

Type I error

A false positive; rejecting the null hypothesis when it is actually true.

Type II error

A false negative; failing to reject the null hypothesis when it is actually false.

Z-score

A statistic that indicates how many standard deviations a data point is from the mean.

Standard error of the mean

Standard deviation of the sampling distribution of the mean.

Central Limit Theorem

The sampling distribution of the mean approaches normality as the sample size increases.

One-tailed test

A hypothesis test that specifies the direction of the expected effect.

Two-tailed test

A hypothesis test that does not specify the direction of the expected effect.

Degrees of freedom

The number of independent values or quantities that can vary in an analysis without violating any constraints.

Paired-samples t-test

A statistical test used to compare the means of two related samples.

Independent-samples t-test

A statistical test that compares the means of two independent groups.

P-value

The probability of obtaining a test statistic at least as extreme as the one that was actually observed, given that the null hypothesis is true.

Alpha level

The threshold significance level in a hypothesis test, commonly set at .05.

Multiplication rule

Used to calculate the probability of two independent events both occurring.

Addition rule

Used to calculate the probability of either of two mutually exclusive events occurring.

Effect size

A measure of the strength of the relationship between two variable or the amount of separation between the population distributions represented by the conditions in an experiment. (always relative to variability). Determined by reality

Confidence interval

A range of values used to estimate the true parameter in the population based on your sample.

steps involved in hypothesis testing

o State null and alternative hypotheses

o Determine characteristics of null

o Calculate appropriate test statistic

o State decision about whether null was rejected

o State the conclusion verbally in terms of finding and variable names

inferential statistic

allows for inferences about population based on a sample

Parametric test

make estimations about population characteristics (ex: assumes distribution follows the pattern of a normal distribution)

z test

Compare a sample of scores with a population mean to determine if there is a significant difference when the population variance is known and the data follows a normal distribution

sampling distribution of the mean

The distribution of sample means over many samples drawn from the same population, which approximates a normal distribution as the sample size increases.

standard error of the mean

is a statistical measure that quantifies the variability of sample means around the true population mean. It estimates how much the sample mean is expected to fluctuate if multiple samples were taken from the same population. It is calculated by dividing the standard deviation of the population by the square root of the sample size.

pooled variance

The weighted mean of the variance for both groups in an independent-sample t-test that accounts for sample sizes, providing a more accurate estimate of the overall variance.

power

probability of rejecting the null hypothesis when it is false

a priori power analysis

conducted before data collection, used to determine the needed sample size to detect an effect of a certain magnitude

post hoc power analysis

conducted after data collection, used to determine the power of a test based on the observed effect size and sample size.

z score vs z test

A z-score represents the number of standard deviations a data point is from the mean, while a z test is a statistical test that uses z-scores to determine if there is a significant difference between sample and population means.