This module will continue the discussion of hypothesis testing, where a specific statement or hypothesis is generated about a population parameter, and sample statistics are used to assess the likelihood that the hypothesis is true. The hypothesis is based on available information and the investigator's belief about the population parameters. The specific tests considered here are called chi-square tests and are appropriate when the outcome is discrete (dichotomous, ordinal or categorical). For example, in some clinical trials the outcome is a classification such as hypertensive, pre-hypertensive or normotensive. We could use the same classification in an observational study such as the Framingham Heart Study to compare men and women in terms of their blood pressure status - again using the classification of hypertensive, pre-hypertensive or normotensive status.
Inferential statistics are procedures which allow researchers to infer or generalize observations made with samples to the larger population from which they are selected. It is different from descriptive statistics in a way that while descriptive statistics remains local to the sample describing the central tendency and variability in the sample, inferential statistics is focused on making statements about the population.
Formulating the Research Hypothesis and Null ..
A coin is tossed 100 times.
The null hypothesis: The coin is fair.
The alternative hypothesis: The coin is not fair.Error:
Type 2 error: You said that the coin is fair when in fact the coin is unfair (accepted the null H when the null H is false).
Type 1 error: You said that the coin is unfair when in reality the coin is fair (you rejected the null H when the null H is true).
There is a preoccupation with Type 1 error in psychological research.
Statistical hypothesis testing - Wikipedia
When we pose a research question, we want to know whether the outcome is due to the treatment (independent variable) or due to chance (in which case our treatment is probably not effective). For example, the claim that tutoring improves math performance generally does not predict exactly how much improvement. Each level of improvement has a different probability associated with it, and it would take a long time and a great deal of effort to specify the probability of each of the possible outcomes that would support our research hypothesis.
contained in the alternative hypothesis
The inferential statistics do not directly address the testable statement (research hypothesis). They address the . Statistically, we test "not." Here are the null hypotheses:
Statistics: Null hypothesis - UC Davis, Psychology
the opposite of the research hypothesis. The null hypothesis states that any effects observed after treatment (or associated with a predictor variable) are due to chance alone. Statistically, the question that is being answered is "If these samples came from the same population with regard to the outcome, how likely is the obtained result?"
the framer of a hypothesis needs to define ..
The null hypothesis can be thought of as the opposite of the "guess" the research made (in this example the biologist thinks the plant height will be different for the fertilizers). So the null would be that there will be no difference among the groups of plants. Specifically in more statistical language the null for an ANOVA is that the means are the same. We state the Null hypothesis as: