Experimental or treatment group : This is the group that receives the experimental treatment, manipulation, or is different from the control group on the variable under study.
Control group :This group is used to produce comparisons. The treatment of interest is deliberately withheld or manipulated to provide a baseline performance with which to compare the experimental or treatment group's performance.
Independent variable:This is the variable that the experimenter manipulates in a study. It can be any aspect of the environment that is empirically investigated for the purpose of examining its influence on the dependent variable.Dependent variable:The variable that is measured in a study. The experimenter does not control this variable.
Random assignment: In a study, each subject has an equal probability of being selected for either the treatment or control group.
Double blind:Neither the subject nor the experimenter knows whether the subject is in the treatment of the control condition.Hypothesis: A hypothesis is a specific statement of prediction. It describes in concrete (rather than theoretical) terms what you expect will happen in your study.
Null Hypothesis: The null hypothesis (H0) is a hypothesis which the researcher tries to disprove, reject or nullify.
The 'null' often refers to the common view of something, while the alternative hypothesis is what the researcher really thinks is the cause of a phenomenon.
An experiment conclusion always refers to the null, rejecting or accepting H0 rather than H1.
The 'null' often refers to the common view of something, while the alternative hypothesis is what the researcher really thinks is the cause of a phenomenon.
An experiment conclusion always refers to the null, rejecting or accepting H0 rather than H1.
Alternative Hypothesis: An
alternative hypothesis (H1) is a statement that directly contradicts a
null hypothesis by stating that that the actual value of a population
parameter is less than, greater than, or not equal to the value stated
in the null hypothesis. The alternative hypothesis states what we
think is wrong about the null hypothesis, Level of significance, or
significance level, refers to a criterion of judgment upon which a
decision is made regarding the value stated in a null hypothesis. The
criterion is based on the probability of obtaining a statistic measured
in a sample if the value stated in the null hypothesis were true.In
behavioral science, the criterion or level of significance is typically
set at 5%. When the probability of obtaining a sample mean is less than
5% if the null hypothesis were true, then we reject the value stated in
the null hypothesis.
Test Statistic: The test statistic is a mathematical formula that allows researchers to determine the likelihood of obtaining sample outcomes if the null hypothesis were true. The value of the test statistic is used to make a decision regarding the null hypothesis.
A p value is the probability of obtaining a sample outcome, given that the value stated in the null hypothesis is true. The p value for obtaining a sample outcome is compared to the level of significance.
Significance, or statistical significance, describes a decision made concerning a value stated in the null hypothesis. When the null hypothesis is rejected, we reach significance. When the null hypothesis is retained, we fail to reach significance.
Type I error is the probability of rejecting a null hypothesis that is actually true.
Type II Error The incorrect decision is to retain a false null hypothesis. This decision is an example of a Type II error, or b error.
A critical value is a cutoff value that defines the boundaries beyond which less than 5% of sample means can be obtained if the null hypothesis is true. Sample means obtained beyond a critical value will result in a decision to reject the null hypothesis.
The rejection region is the region beyond a critical value in a hypothesis test. When the value of a test statistic is in the rejection region, we decide to reject the null hypothesis; otherwise, we retain the null hypothesis.
The z statistic is an inferential statistic used to determine the number of standard deviations in a standard normal distribution that a sample mean deviates from the population mean stated in the null hypothesis.
The obtained value is the value of a test statistic. This value is compared to the critical value(s) of a hypothesis test to make a decision. When the obtained value exceeds a critical value, we decide to reject the null hypothesis; otherwise, we retain the null hypothesis.
Directional tests, or one-tailed tests, are hypothesis tests where the alternative hypothesis is stated as greater than (>) or less than (<) a value stated in the null hypothesis. Hence, the researcher is interested in a specific alternative from the null hypothesis.
For a single sample, an effect is the difference between a sample mean and the population mean stated in the null hypothesis. In hypothesis testing, an effect is insignificant when we retain the null hypothesis; an effect is significant when we reject the null hypothesis.
Effect size is a statistical measure of the size of an effect in a population, which allows researchers to describe how far scores shifted in the population, or the percent of variance that can be explained by a given variable.
Nondirectional (two-tailed) tests are hypothesis tests where the alternative hypothesis is stated as not equal to (≠). So we are interested in any alternative from the null hypothesis.
Directional (one-tailed) tests are hypothesis tests where the alternative hypothesis is stated as greater than (>) or less than (<) some value. So we are interested in a specific alternative from the null hypothesis.
A Type III error occurs with one-tailed tests, where the researcher decides to retain the null hypothesis because the rejection region was located in the wrong tail.
The “wrong tail” refers to the opposite tail from where a difference was observed and would have otherwise been significant.
What is Validity? Validity is the extent to which a test measures what it claims to measure. It is vital for a test to be valid in order for the results to be accurately applied and interpreted. Validity isn’t determined by a single statistic, but by a body of research that demonstrates the relationship between the test and the behavior it is intended to measure. There are three types of validity:
Content validity:When a test has content validity, the items on the test represent the entire range of possible items the test should cover. Individual test questions may be drawn from a large pool of items that cover a broad range of topics. In some instances where a test measures a trait that is difficult to define, an expert judge may rate each item’s relevance. Because each judge is basing their rating on opinion, two independent judges rate the test separately. Items that are rated as strongly relevant by both judges will be included in the final test.Criterion-related Validity: A test is said to have criterion-related validity when the test has demonstrated its effectiveness in predicting criterion or indicators of a construct. There are two different types of criterion validity:
Concurrent
Validity occurs when the criterion measures are obtained at the same
time as the test scores. This indicates the extent to which the test
scores accurately estimate an individual’s current state with regards to
the criterion. For example, on a test that measures levels of
depression, the test would be said to have concurrent validity if it
measured the current levels of depression experienced by the test taker.
Predictive Validity occurs when the criterion measures are obtained at a time after the test. Examples of test with predictive validity are career or aptitude tests, which are helpful in determining who is likely to succeed or fail in certain subjects or occupations.
Predictive Validity occurs when the criterion measures are obtained at a time after the test. Examples of test with predictive validity are career or aptitude tests, which are helpful in determining who is likely to succeed or fail in certain subjects or occupations.
Construct Validity
A test has construct validity if it demonstrates an association between the test scores and the prediction of a theoretical trait. Intelligence tests are one example of measurement instruments that should have construct validity.
A test has construct validity if it demonstrates an association between the test scores and the prediction of a theoretical trait. Intelligence tests are one example of measurement instruments that should have construct validity.
Hypothesis Testing
The method in which we select samples to learn more about characteristics in a given population is called hypothesis testing. Hypothesis testing is really a systematic way to test claims or ideas about a group or population.
The method in which we select samples to learn more about characteristics in a given population is called hypothesis testing. Hypothesis testing is really a systematic way to test claims or ideas about a group or population.
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