This document provides an introduction to inferential statistics, including key terms like test statistic, critical value, degrees of freedom, p-value, and significance. It explains that inferential statistics allow inferences to be made about populations based on samples through probability and significance testing. Different levels of measurement are discussed, including nominal, ordinal, and interval data. Common inferential tests like the Mann-Whitney U, Chi-squared, and Wilcoxon T tests are mentioned. The process of conducting inferential tests is outlined, from collecting and analyzing data to comparing test statistics to critical values to determine significance. Type 1 and Type 2 errors in significance testing are also defined.
INFERENTIAL STATISTICS: AN INTRODUCTIONJohn Labrador
For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study.
INFERENTIAL STATISTICS: AN INTRODUCTIONJohn Labrador
For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study.
Hypothesis Testing is important part of research, based on hypothesis testing we can check the truth of presumes hypothesis (Research Statement or Research Methodology )
Hypothesis Testing is important part of research, based on hypothesis testing we can check the truth of presumes hypothesis (Research Statement or Research Methodology )
Statistics is the science of dealing with numbers.
It is used for collection, summarization, presentation and analysis of data.
Statistics provides a way of organizing data to get information on a wider and more formal (objective) basis than relying on personal experience (subjective).
Quick reminder ordinal or scaled or nominal porportionalKen Plummer
This is learning module for a decision point within a decision model for statistics as part of a teaching methodology called Decision-Based Learning developed at Brigham Young University in Provo, Utah, United States
Basics of Hypothesis testing for PharmacyParag Shah
This presentation will clarify all basic concepts and terms of hypothesis testing. It will also help you to decide correct Parametric & Non-Parametric test for your data
Fundamental of Statistics and Types of CorrelationsRajesh Verma
Fundamental of Statistics and Types of Correlations. Pearson r, Point Biserial, Phi Coefficient, Biserial, Tetrachoric, Spearman Rank Difference, Kendall's tau, Inferential Statistics, Descriptive Statistics
4. Descriptive Statistics vs. Inferential Statistics
Allows us to draw
Allow us to say whether
conclusions
difference is significant
Through use of graphs
This difference
Is significant
5. Probability
Inferential tests use probability to ascertain the
likelihood that a pattern of results could have
arisen by chance.
If the probability of the results occurring by
chance is below a certain level we assume these
results to be significant
6. Chance
We can state how certain
we are the results are not Real
due to chance difference
7. P-levels/Significance Levels
P ≤0.10
C
H P ≤0.05
A
N P ≤0.01
C
E P ≤0.001
We can also write these as 10%, 5%, 1%, 0.1%
8. Significant?
If our test is significant we can
Reject our null hypothesis and accept our
alternative/experimental hypothesis
If our test is not significant we can
Accept our null hypothesis and reject our
alternative/experimental hyp
10. Levels of data: nominal
• Which newspaper paper do you read
regularly?
• We can put these into categories.
11. Levels of Data: ordinal
• What grade did you get for each of your
gcse’s?
• These can be put in order… highest to lowest
12. Levels of data: interval
• How quick is your reaction time?
• We can measure and compare the exact time
because the intervals on the ruler are equal.
13. Inferential Tests
Which test to use depends upon a number of
factors:
• The type of data
• Type of research design (RM vs. IG)
• One tailed or two tailed test
15. Process
data
Complicated arithmetic
Produce test statistic
16. Sig levels ½’d for one
tailed test
Compare test
Statistic
with critical values
for that test
To determine significance
level
critical value: Value that test statistic must reach in order for null hyp to be rejected
18. Type 1 and Type 2 Errors
Type 1 error
Rejecting a null hypothesis when we should not
P level too tight
Type 2 error
Accepting a null hypothesis when we should not
P level too loose
