Chapter 6 7 & 8 Flashcards
uses sample data to answer questions about the population
Inferential statistics
are built around the concept of probability
Inferential procedures
- defines the relationships between samples and populations
- is the likelihood of an event happening.
-We calculate it by dividing the number of ways the event can happen by the total number of possible outcomes.
Probability
to begin with a sample and answer a general question about the population
The goal of inferential statistics
The first stage to accomplish the goal of inferential statistics.
involves developing probability as a bridge from populations to samples
The second stage to accomplish the goal of inferential statistics.
involves reversing the probability rules to allow moving from samples to populations
requires outcomes obtained through random sampling
Probability
requires each individual to have an equal chance of selection
Simple Random Sample
-is necessary when selecting multiple individuals, with probabilities staying constant between selections
- is assumed to be used in statistical applications, also known as Random Sampling
Independent Random Sampling
Two Requirements for random sampling
• First requirement ensures no bias in selection process and prohibits application of probability definition in situations where outcomes are not equally likely
• Second requirement demands sampling with replacement to keep probabilities constant between selections
is the basis for many statistics, but other definitions and sampling techniques also exist.
Random sampling with replacement
concerns a population of scores displayed in a frequency distribution graph.
Probability
represents the entire population, and different proportions represent different proportions of the population.
The graph
T or F
Probabilities and proportions are equivalent.Thus, it is possible to represent probabilities as proportions of the graph.
true
is a symmetrical bell-shaped curve with the highest frequency in the middle and frequencies tapering off towards both extremes.
The normal distribution
Statisticians use these to identify sections of a normal distribution, with each section representing a proportion of the population.
Z-scores
is a good model for many naturally occurring distributions and is guaranteed in some circumstances.
The normal Distribution
questions about a normal distribution can be answered by using known proportions and information about the population mean and standard deviation.
Probability
is a more complete tool for finding proportions of a normal distribution.
- is structured into four columns, which identify z-score values, the proportion in the body, the proportion in the tail, and the proportion between the mean and the z-score.
A unit normal table
T or F
To use the unit normal table, you must find the row that corresponds to the z-score value you are interested in.
true
T or F
There are a few important facts to keep in mind when using the unit normal table, including that the body always corresponds to the larger part of the distribution, and the tail always corresponds to the smaller part of the distribution.
true
is the percentage of individuals with scores less than or equal to a specific score.
Percentile rank
A score referred to by its percentile rank
Percentile
T or F
Probability links samples to populations and is the foundation for inferential statistics.
true
can be set to separate the most likely 95% of the samples in the middle of the distribution from the extremely unlikely 5% in the tails.
Boundaries
Where does the boundaries set,provide objective objective criteria for deciding whether the treated sample is noticeably different from the original population
z = ±1.96
