All terms in this list:
Descriptive Statistics: A branch of statistics dealing with summarization and description of collections of data—data sets, including the concepts of arithmetic mean, median, mode, and quantile.
Inferential Statistics: A branch of statistics studing statistical inference—drawing conclusions about a population from a random sample drawn from it, or, more generally, about a random process from its observed behavior during a finite period of time.
Mean: *the average score or value * Mean of a sample: M = (∑X)/n * The main problem associated with the mean value of some data is that it is sensitive to outliers.
Median: The median is simply the middle value among some scores of a variable. (no standard formula for its computation) • If there is an even number of scores: take the mean of the two middle numbers.
Mode: • The most frequent response or value for a variable. • Multiple modes are possible: bimodal or multimodal.
Dispersion: Measures of dispersion give us information about how much our scores vary from the mean, • If they don’t, it is difficult to infer anything from the data • Dispersion is also known as the spread or variability
Range: r = h – l – Where h is high and l is low • In other words, the range gives us the value between the minimum and maximum values of a variable.
Standard deviation: A measure of how spread out data values are around the mean, defined as the square root of the variance. • The more spread apart the data, the higher the standard deviation • The more outliers in the data, the higher the standard deviation
parameter: A variable kept constant during an experiment, calculation or similar.
Sampling Error: The error caused by observing a sample instead of the whole population.
Correlation: One of the several measures of the linear statistical relationship between two random variables, indicating both the strength and direction of the relationship.
Pearson's r: -Testing hypothesis for two continuous variables -Null hypothesis Procedure
Chi Square: Testing hypothesis for two categorical variables
Student's t-test: * Testing hypothesis for one categorical and one continuous variable * any statistical hypothesis test in which the test statistic has a Student's t-distribution if the null hypothesis is true
ANOVA: * Testing hypothesis for one categorical (multiple levels) and one continuous variable * • Can be used when categorical variable has more than two categories (e.g., IV = Freshmen, Sophomore, Junior, Senior) - and - • Can include more than one indepen
Null Hypothesis: A hypothesis set up to be nullified or refuted in order to support an alternative hypothesis. When used, the null hypothesis is presumed true until statistical evidence in the form of a hypothesis test indicates otherwise. Therefore, the null and the alte
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