Mid Flashcards
Hypothesis
-states the tentative relationship between independent and
dependent variables, along with a prediction of the outcome.
-is a tentative prediction or explanation of the relationship
between two variables.
Null Hypothesis (Ho)
-Assumed to be true until there is evidence to suggest otherwise.
-Implies that there is no signicant difference between variables.
Alternative Hypothesis (Ha)
-This is the statement that one wants to conclude.
-Also called the research hypothesis.
Inferential Statistics
is the practice of using sample data to draw conclusions or
make predictions about larger sample or population.
Statistical significance
a determination of the null
hypothesis, which suggests that the results are due to chance
alone. A data set provides statistical significance when the pvalue is sufficiently small.
P value
is the probability of null hypothesis being true
• 5% - statistical significance level / α.
• probability that the null hypothesis is not true
• 95% - probability that the hypothesis is true
significance level.
Alpha (α) -the criterion for statistical significance that we
set for our analyses.
- When null is true, but was rejected.
- 0.05 is the commonly used significance level.
Types of Hypothesis Test
- One-tailed (or one-sided) test:
o Tests for the significance of an effect in only one
direction, either positive (Right-tailed) or negative
(Negative-tailed). - Two-tailed (or two-sided) test:
o Tests for the significance of an effect in both
directions, allowing for the possibility of a
positive or negative effect.
Logic of Hypothesis Testing
• To measure the variables involved,
• Examine the relationship between them and;
• Whether to reject or fail to reject the null
hypothesis.
Probability of Errors
Type I error (False Positive)
• If we reject the null hypothesis when it is true.
Type II error (False negative)
• If we failed to reject or incorrectly accepted the null
hypothesis when it is false.
Alpha (a)
• The probability of committing a Type I error.
• Also known as the significance level.
• α – null is true, but was rejected.
Probability
The branch of mathematics concerning events and
numerical descriptions of how likely they are to occur. In simple
words, probability is simply how likely something is to happen.
Any activity with an observable result.
Experiment
The result of the experiment.
Outcome
The set of all possible outcomes of an experiment
Sample space
The set of all possible outcomes of an experiment
Sample space
Event
A subset of the sample space
Normal distribution,
also known as the Gaussian distribution, is a probability distribution that
is symmetric about the mean, showing that data near the mean are more frequent in
occurrence than data far from the mean. The normal distribution appears as a “bell curve”
when graphed.
The standard normal distribution
also called the z-distribution, is a special normal distribution
where the mean is 0 and the standard deviation is 1
A unit normal table
(also called the standard normal table or z-score table) shows the percentage or
probability of values that fall below a given z-score in a standard normal distribution.
Population
Refers to the entire group or set of individuals, objects, or events that possess
specific characteristics and are of interest to the researcher. It represents the larger
population from which a sample drawn.
Sample
Is a subset of
individuals from a larger population
Probability Sampling
Is a sampling
technique where a researcher sets a
selection of a few criteria and chooses
members of a population randomly.
Non-probability Sampling
Is a sampling
method in which not all members of the
population have an equal chance of
participating in the study.
In a simple random sampling
every member of the population has an equal chance of being
selected
systematic sampling
every member of the population is listed with a number, but
instead of randomly generating numbers, individuals are chosen at regular intervals
In stratified sampling
Researchers divide the population into subgroups called strata
based on characteristics that they share (e.g., race, gender, educational attainment).
Once divided, each subgroup is randomly sampled using another probability sampling
method.
Cluster sampling
t is a sampling method where the researcher divides the entire population into
separate groups, or clusters such as districts or schools, and then randomly select some
of these clusters as your sample
convenience sample
Simply includes the individuals who happen to be most
accessible to the researcher. This is an easy and inexpensive way to gather initial data
voluntary response sample
A type of sample made up of self-chosen participants. These participants volunteer
to take part in different research studies to share their opinions on topics that interest
them.
purposive sample
It is also called judgmental sampling. It involves the researcher using their judgement to select a
sample that is most useful to the purposes of the research. It is often used in qualitative research,
where the researcher wants to gain detailed knowledge about a specific phenomenon rather than
make statistical inferences.
purposive sample
It is also called judgmental sampling. It involves the researcher using their judgement to select a
sample that is most useful to the purposes of the research. It is often used in qualitative research,
where the researcher wants to gain detailed knowledge about a specific phenomenon rather than
make statistical inferences.
Qouota sampling
In this sampling, researchers create a convenience sample involving individuals that
represent a population. Researchers choose these individuals according to specific
traits or qualities. They decide and create quotas so that the market research samples
can be useful in collecting data.
Snowball sampling
If the population is hard to access, this can be used to recruit
participants via other participants.
Sampling Distribution
The probability distribution that describes the
probability for each mean of all the samples with
same sample size n is called
T- Test Statistic
The t-test is a statistical test procedure that tests whether there is a
significant difference between the means of two groups. It helps to
assess whether the differences observed between the groups are likely
due to a chance or if they represent true differences in the population
being studied.
Independent Samples t-test
A procedure compares means for two groups of
cases and automates the t test effect size computation. It
exist if no case or person from one group can be assigned to a case or person
from the other group. The values come from two or
more different groups
Dependent (paired sample) T-Test
Comparing the means of two related groups, such as before and after
measurements in the same group or measurements from matched pairs. In a
dependent sample, the measures are related.
ANOVA
stands for Analysis of Variance or F-test. It is a statistical method used to analyze the differences between group means and determine whether these differences are statistically significant. It is commonly used in research studies to compare means across three or more groups.
ANOVA
examines the variation in a dataset and assesses whether the observed differences between groups are statistically significant. It helps determine if the groups are drawn from the same population or if the treatments have an effect.
A one-way ANOVA
evaluates the impact of a sole factor on a sole response variable. It determines whether all the samples are the same. used to determine whether there are any statistically significant differences between the means of three or more independent groups
A two-way ANOVA
An extension of the one-way ANOVA. With a one-way, you have one independent variable affecting a dependent variable. There are two independents. It allows a company to compare worker productivity based on two independent variables
Correlational Research
Type of res. method that is used to understand the relationship between two or more variables. How two variables change without manipulating them
Positive Correlation
An increase in one variable is associated with an increase in another variable.
Negative Correlation
An increase in one variable is associated with an decrease in another variable.
Zero Correlation
No relationship between the variables
Correlation Coefficient
Used to quantify and analyze the relationship between variables.
A numerical measure that indicates strength and direction of linear relationship between two variables.
Pearson’s Sample Correlation coefficient
Denoted by r, a test statistic that measures strength of the linear relationship between two variables
