Terms to study Flashcards
(32 cards)
z-test
z-test A z-test is a statistical test to determine whether two population means are different when the variances are known and the sample size is large. A z-test is a hypothesis test in which the z-statistic follows a normal distribution. A z-statistic, or z-score, is a number representing the result from the z-test.
Sample
Sample In statistics, a sample is an analytic subset of a larger population. The use of samples allows researchers to conduct their studies with more manageable data and in a timely manner. Randomly drawn samples do not have much bias if they are large enough, but achieving such a sample may be expensive and time-consuming.
p-value
p-value The p-value, or probability value, tells you how likely it is that your data could have occurred under the null hypothesis. It does this by calculating the likelihood of your test statistic, which is the number calculated by a statistical test using your data.
µ
The symbol ‘μ’ represents the population mean.
F
An F-value is the ratio of two variances, or technically, two mean squares. Mean squares are simply variances that account for the degrees of freedom (DF) used to estimate the variance. F-values are the test statistic for F-tests. Learn more about Test Statistics.
One-sample t-test
One-sample t-test
A one sample test of means compares the mean of a sample to a pre-specified value and tests for a deviation from that value. For example we might know that the average birth weight for white babies in the US is 3,410 grams and wish to compare the average birth weight of a sample of black babies to this value.
Population
Population
What Is Population? A population is the complete set group of individuals, whether that group comprises a nation or a group of people with a common characteristic. In statistics, a population is the pool of individuals from which a statistical sample is drawn for a study.
Critical region
Critical region
A critical region, also known as the rejection region, is a set of values for the test statistic for which the null hypothesis is rejected. i.e. if the observed test statistic is in the critical region then we reject the null hypothesis and accept the alternative hypothesis.
σ
σ
The symbol ‘σ’ represents the population standard deviation. The term ‘sqrt’ used in this statistical formula denotes square root. The term ‘Σ ( Xi – μ )2^ used in the statistical formula represents the sum of the squared deviations of the scores from their population mean.
Z
Z
A z-score, or z-statistic, is a number representing how many standard deviations above or below the mean population the score derived from a z-test is. Essentially, it is a numerical measurement that describes a value’s relationship to the mean of a group of values.
Two-sample independent t-test
Two-sample independent t-test
The independent t-test, also called the two sample t-test, independent-samples t-test or student’s t-test, is an inferential statistical test that determines whether there is a statistically significant difference between the means in two unrelated groups.
Alpha level
Alpha level
In statistical tests, statistical significance is determined by citing an alpha level, or the probability of rejecting the null hypothesis when the null hypothesis is true. For this example, alpha, or significance level, is set to 0.05 (5%).
SS
SS
The sum of squares is a measure of deviation from the mean. In statistics, the mean is the average of a set of numbers and is the most commonly used measure of central tendency. The arithmetic mean is simply calculated by summing up the values in the data set and dividing by the number of values.
r
r
The correlation coefficient (r) is a statistic that tells you the strength and direction of that relationship. It is expressed as a positive or negative number between -1 and 1. The value of the number indicates the strength of the relationship: r = 0 means there is no correlation.
Repeated measures t-test
Repeated measures t-test
The t-test assesses whether the mean scores from two experimental conditions are statistically different from one another. A repeated-measures t-test (also known by other names such as the ‘paired samples’ or ‘related’ t-test) is what you should use in situations when your design is within participants.
Sample size
Sample size
Sample size refers to the number of participants or observations included in a study. This number is usually represented by n. The size of a sample influences two statistical properties: 1) the precision of our estimates and 2) the power of the study to draw conclusions.
Null hypothesis
Null hypothesis
The null hypothesis is a typical statistical theory which suggests that no statistical relationship and significance exists in a set of given single observed variable, between two sets of observed data and measured phenomena.
s
s
The sample standard deviation (s) is the square root of the sample variance and is also a measure of the spread from the expected values. In its simplest terms, it can be thought of as the average distance of the observed data from the expected values.
n
n
Sample size refers to the number of participants or observations included in a study. This number is usually represented by n. The size of a sample influences two statistical properties: 1) the precision of our estimates and 2) the power of the study to draw conclusions.
One-way ANOVA
One-way ANOVA
One-Way ANOVA (“analysis of variance”) compares the means of two or more independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different. One-Way ANOVA is a parametric test.
Population parameter vs sample statistic
Population parameter vs sample statistic
sample statistic. When you collect data from a population or a sample, there are various measurements and numbers you can calculate from the data. A parameter is a measure that describes the whole population. A statistic is a measure that describes the sample.
Alternate hypothesis
Alternate hypothesis
The alternative hypothesis is a statement used in statistical inference experiment. It is contradictory to the null hypothesis and denoted by Ha or H1. We can also say that it is simply an alternative to the null. In hypothesis testing, an alternative theory is a statement which a researcher is testing.
x ̅
x ̅
X-bar in statistics is a symbol for the sample mean. Given a sample of n observations of numbers, the sample mean is found by adding up all of the observations, then dividing by the total number of observations (n).
k
k
The “k” in that formula is the number of cell means or groups/conditions.
