BIOE Flashcards
Population, constant regardless of sample/ unkown
PARAMETER
Variable, sample
STATISTICS
Is the act of paying attention to something in order to gain information
OBSERVATION/DESCRIPTIVE
LOGICAL INTERPRETATION OR EXPLANATION OF THE OBSERVATION
INFERENCE
USES FIVE SENSES
OBSERVATION/DESCRIPTIVE
MENTAL PROCESS
INFERENTIAL
IS A CONCLUSION REACHED ON THE BASIS OF EVIDENCE AND REASONING
INFERENTIAL
FACILITATE UNDERSTANDING, ANALYSIS, INTERPRETATION OF DATA
DESCRIPTIVE
ESTIMATION OF PARAMETERS AND HYPOTHESIS TESTING
INFERENTIAL
COMPUTATION OF MEASURES OF CENTRAL TENDENCY AND VARIABILITY. TABULATION AND GRAPHICAL PRESENTATION
DESCRIPTIVE
METHODS OF SUMMARIZING AND PRESENTING DATA
DESCRIPTIVE
Process of generalizing conclusions about the target population on the basis of results obtained from a sample.
STATISTICAL INFERENCE
Uses sample statistics to
determine the unknown
parameters of the population
STATISTICAL INFERENCE
measures computed using data
from the entire population
PARAMETERS
measures computed using data
from the sample
STATISTICS
Process by which the statistic computed from a random sample is
used to approximate the corresponding parameter in the population.
ESTIMATION
PROCESS OF ESTIMATION
- GET DATA FROM SAMPLE RESPONDENTS
- CALCULATE THE SUMMARY MEASURES (STATISTIC)
- USE STATISTIC TO ESTIMATE PARAMETER VALUE
Process of deciding whether or not a hypothesis about the target
population is true based on the sample data.
HYPOTHESIS TESTING
- a statement about the population
- usually something about the value of the parameters
HYPOTHESIS
PROCESS OF HYPOTHESIS TESTING
- GET THE DATA FROM THE SAMPLE
- CALCULATE STATISTICS
- APPLY STATISTICAL TESTS
- DECISION
Frequency distribution of the statistic computed from each of all the
possible samples of the population
SAMPLING DISTRIBUTION OF A STATISTIC
Knowing the properties of the sampling distribution will help in:
- ESTIMATING POPULATION PARAMETERS
2. TEST HYPOTHESIS ABOUT POPULATION PARAMETERS
TF The sampling distribution of statistical mean reflects the frequency distribution of
sample means of all possible samples of size n.
T
TF Population parameter is generally known
F
TF Sampling distribution cannot be constructed in reality
T
TF The best estimate of the population mean (U) is the mean of the sample mean (x)
T
Only applicable when the sample is RANDOM
POINT ESTIMATE
point estimate Identify:
• Variable:
• Population:
• Sample:
QUANTITATIVE, PARAMETER WANTED, RANDOM
TF statistical mean is an estimate of population mean and they are equal
F
Interval constructed is called:
CONFIDENCE LEVEL
- from literature
- from previous studies
POPULATION STANDARD DEVIATION Z TEST
IF POPULATION SD IS ABSENT WE WILL USE?
STATISCAL SD T TEST
Variable of interest of population proportion is
QUALITATIVE
TF In sampling distribution of population propotions, The sampling distribution of p reflects the frequency distribution of
population proportions of all possible samples of size n
F- POPULATION - T- SAMPLE
TF The distribution is normally distributed in sampling distribution of population proportion.
T
decision is towards
null hypothesis
is towards alternative hypothesis
conclusion
• A non-parametric test that test the association between two
variables.
CHI-SQUARE TEST OF INDEPENDENCE
• A parametric test used in determining relationship between
two set of data.
Pearson r (Bivariate - 2 variables)
A non-parametric test used to find out if there is a significant
relationship between 2 variables.
Spearman rank (rs)
- The joint distribution of the variables x and y is also a normal distribution
Pearson r (Bivariate - 2 variables)
Under correlation analysis
Spearman rank (rs) Pearson r (Bivariate - 2 variables)
• Qualitative Description of Coefficient of Correlation
Pearson r (bivariate - 2 variables)
– used to measure both the strength
and the direction of the relationship between two quantitative variable. Done through the computation of the correlation
coefficient.
Correlation analysis
used coefficient in interval/ratio
pearson r
used coefficient for ordinal data
spearman rank
TF in pearson r, the closer the value to one, the greater the strength of linear relationship
T
The value in pearson r indicates?
magnitude
- we will test for the existence of relationship or association between two qualitative variables in a single population.
CHI-SQUARE TEST OF INDEPENDENCE
We will we will test whether or not variable x is associated with variable y
CHI-SQUARE TEST OF INDEPENCE
