| 1-tailed test |
The probability of Type I error is included in one tail of
the sampling distribution. Generally used when the direction of the
difference between two populations can be supported
by theory or other knowledge gained prior to testing for statistical
significance. |
| 2-tailed test |
The probability of Type I error is included in both tails of
the sampling distribution (e.g.,
alpha .05 means .025 is in one tail and .025 is in the other tail).
Generally
used when the direction of the difference between two populations
cannot
by supported by theory or other knowledge gained prior to testing for
statistical
significance. |
| Alpha |
The probability of a Type I error. Represents the threshold
for claiming statistical significance. |
| Association |
Changes
in one variable are accompanied by changes in another variable. |
| Central Limit
Theorem |
As sample
size increases, the distribution approximates a normal distribution and
is
usually close to normal at a sample size of 30. |
| Critical Value |
The point
on the x-axis of a sampling distribution that is equal to alpha. It is
interpreted as standard error. As an example, a critical value of 1.96
is interpreted as 1.96 standard errors above the mean of the sampling
distribution. |
| Continuous Variable |
A variable
than can take on any numerical value even between one value and
another. Grade point average,
distance in kilometers, loan interest rates. |
| Dependent Variable |
A measure
not under the control of the researcher that reflects responses caused
by
variations in another measure (the independent variable). |
| Descriptive
Statistics |
Statistics that classify and summarize numerical data. |
| Discrete Variable |
A variable
that is limited to a finite number of values. Such as religion or
number of parks in a city
(you can't have 1.5 parks) |
| Homoscedasticity |
The variance of the Y scores in a correlation are uniform
for the values of the X scores. In other words, the Y scores are
equally spread above and below the regression line. |
| Independent Variable |
A measure
that can take on different values which are subject to manipulation by
the
researcher. |
| Inferential Statistics |
Statistics that use characteristics of a random sample along
with measures of sampling error to predict the true values in a larger
population. |
| Interpretation Bias |
Errors in
data collection that occur when knowledge of the results of one test
affect the interpretation of a second test. |
| Interval Data |
Objects
classified by type or characteristic, with logical order and equal
differences
between levels of data. |
| Kurtosis |
The
peakedness of a distribution. Leptokurtic is more peaked, Mesokurtic is
a normal distribution, and Platykurtic is a flatter distribution. |
| Mean |
The arithmetic average of the scores in a sample
distribution. |
| Median |
The point
on a scale of measurement below which fifty percent of the scores fall. |
| Measurement Scale |
A
reflection of how well a variable and/or concept can be measured.
Generally categorized in
order of precision as nominal, ordinal, interval, and ratio data. |
| Mode |
The most
frequently occurring score in a distribution. |
| Mu |
The arithmetic average of the scores in a population. |
| Nominal |
Objects
classified by type or characteristic. |
| Normal
Distribution |
A frequency distribution of scores that is symmetric about
the mean, median, and mode. |
| Ordinal Data |
Objects
classified by type or characteristic with some logical order. |
| Parameter |
The measure of a population characteristic. |
| Population |
Contains
all members of a group. |
| Power |
Power is
1-Beta and is
defined as the probability of correctly finding statistical
significance. A common value for power is .80 |
| P-value |
The probability of a Type I error |
| Random Sampling |
Each and
every element in a population has an equal opportunity of being
selected. |
| Ratio Data |
Objects
classified by type or characteristic, with logical order and equal
differences
between levels, and having a true zero starting point. |
| Reliability |
The extent to which a measure obtains similar results over
repeat trials. |
| Research Question |
Defines
the purpose of
the study by clearly identifying the relationship(s) the researcher
intends to investigate. |
| Response Bias |
Errors in
data collection caused by differing patterns and completeness of data
collection that are dominated by a specific subgroup within the sample. |
| Response Variable |
The
measure not controlled in an experiment. Commonly known as the
dependent variable. |
| Sample |
A subset
of a population. |
| Sample
Distribution |
A frequency distribution of sample data. |
| Sampling
Distribution |
A probability distribution representing an infinite number
of sample distributions for a
given sample size. |
| Skewness |
Skewness
provides an indication of the how asymmetric the distribution is for a
given sample. When estimated using the third moment, a value of 0
indicates a normal asymmetric distribution. A positive value
indicates a positive skew (the right tail is longer than the
left). A negative value indicates a negative skew (the left tail
is longer than the right). Skewness values greater than 1 or less
than
-1 indicate a non-normal distribution. |
| Spurious
Correlation |
The strength and direction of an association between an
independent and dependent variable depends on the value of a third
variable. |
| Statistic |
Measure
of a sample characteristic. |
| Statistical
Significance |
Interpreted as the probability of a Type I error. Test
statistics that meet or exceed a critical value are interpreted as
evidence that the differences exhibited in the sample statistics are
not due to random sampling error and therefore are evidence supporting
the conclusion there is a real difference in the populations
from which the sample data were obtained. |
| Type I Error |
Rejecting
a true null hypothesis. Commonly interpreted as the probability of
being wrong
when concluding there is statistical significance. Also referred to as
Alpha,
p-value, or significance. |
| Type II Error |
Retaining
a false null hypothesis. Also referred to as Beta. |
| Unit of Analysis |
The object
under study. This could be people, schools, cities, etc. |
| Validity |
The extent to which a measure accurately represents an
abstract concept. |
| Variable |
A characteristic that can form different values from one
observation to another. |
