Statistical Analysis Flashcards
What is Statistical Analysis
Is the application of statistical methods to a sample of data in order to develop an understanding of what that data represents.
Two types of Statistics
- Descriptive Statistics.
- Inferential Statistics.
(Type of Statistic) Descriptive Statistics
The objective is to make it easier to understand and visualize raw data allowing interpretation without making conclusions regarding any hypothesis that were or can be made.
Common measures of Descriptive statistical Analysis
- Central Tendency.
- Dispersion.
- Skewness.
(type of Descriptive statistical analysis) Central Tendency
Locate the center of a data sample. Common measures include: Mean, Median, and Mode
Mean
would be the sum of all total of scores divided by the size of the data set, the 25 students.
Median
Is the measure that finds the middle value in a set of data, where one half of the data can be smaller or equal to the median and the other half can larger or equal. Median are not affected by outliers.
Mode
is the value that occurs more frequently in a set of observation. If the most common score of the 25 students is 72%, then that is your Mode.
(type of Descriptive statistical analysis) Dispersion
Is the measure of variability in a dataset. Common measures of statistical dispersion are:
- Variance.
- Standard Deviation.
- Range.
Variance
defines how far away the data points fall from the center. When a distribution has lower variability, the values in the data set are more consistent. However, when the variability is higher, the data pints are more dissimilar and extreme values become more likely.
Standard Deviation
tells you how tightly your data is clustered around the Mean.
Range
gives you the distance between the smallest and largest values in your dataset.
(type of Descriptive statistical analysis) Skewness
is the measure of whether the distribution of values is symmetrical around a central values or skewed left or right. Skewed data can affect which types of analyses are valid to perform
(Type of Statistic) Inferential Statistics
takes data from a sample to male inferences about the large population from which the sample was drawn. Using methods of inferential statistics helps draw generalizations that apply the results of the sample to the population as a whole
Common methodologies of Inferential Statistics include:
- Hypothesis Testing.
- Confidence Intervals.
- Regression Analysis.
Hypothesis Testing
involves the procedure of deciding whether the results of a research study supports a particular theory which applies to a population.
Confidence Intervals
refers to the probability that a population parameter will fall between a set of values for a certain proportion of times.
Regression Analysis
incorporates hypothesis tests that help determine whether the relationships observed in the sample data actually exist in the population rather than just the sample.
