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Introduction to Psychology Research Methods Types of research, ..

Although not statistically significant there was a consistent trend for more time consumed per data value (minutes +/- SD): Adult 0.50 +/- 0.17 min vs.

Causal and associative hypotheses in psychology: Examples from eyewitness testimony ..

Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data. Descriptive statistics do not, however, allow us to make conclusions beyond the data we have analysed or reach conclusions regarding any hypotheses we might have made. They are simply a way to describe our data.

Descriptive vs causal hypothesis | scholarly search

Descriptive vs. Hypothesis-driven, part II | Blue Lab Coats

The probability of a type II error is denoted by the Greek letter β. To calculate it, we need to specify, in the alternative hypothesis, a value for the parameter to be tested (in our example, a value for π). Generic alternative hypotheses (different from, greater than, less than) are not useful. In practice, the β-value for a set of alternative hypotheses is of interest, or its complement, which is called the statistical power of the test. For example, fixing the α-error value at 5%, from , we find:

In making decisions about statistical hypotheses, we can incur two kinds of errors: a type I error, rejection of the null hypothesis when it is true; or a type II error, acceptance of the null hypothesis when it is false. The probability level, or p-value, is the probability of a type I error, denoted by the Greek letter α. This is calculated from the probability distribution of the observations under the null hypothesis. It is customary to predefine an α-error level (e.g., 5%, 1%) and reject the null hypothesis when the result of our observation has a probability equal to or less than this so-called critical level.

Descriptive statistics : Hypothesis testing - BrainMass

Nov 18, 2012 · What is the difference between Descriptive and Inferential Statistics


In the introduction of the study, the researcher proposed (via the hypotheses) a relationship(s) between two or
more variables. The level of significance, or probability level, statistically tells the researcher how confident she/he
is with these results, and whether to reject or support this hypotheses. The level of significance is commonly set at the p

Causal
Exploratory – Major emphasis is on gaining ideas and insights.

Descriptive – Emphasis on determining the frequency with which something occurs, or the covariance between two variables.

Causal – Emphasis on determining cause-and-effect relationships.
Exploratory Research
Provide better understanding of a situation.

Not designed to come up with final answers or decisions.

Used to produce a hypothesis.*
*(statement that describes how two or more variables are related)

Basic Example
Particular line of vehicles dropped in sales last quarter.

You conduct interviews with potential car buyers and notice that they
seem to be more excited about new styles.

Hypothesis = Style preferences have changed, resulting in lower sales.

Example Video
*Note – When you conduct interviews or surveys, try to avoid asking close-ended questions.

Descriptive Statistics, Hypothesis Test, and Linear Regression
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Understanding Descriptive and Inferential Statistics

The population-sample paradigm implies that validity can be assured by the way the sample is selected from the population. Random or probabilistic sampling is the usual strategy: if each member of the population has the same probability of being included in the sample, then, on average, our sample should be representative of the population and, moreover, any deviation from our expectation could be explained by chance. The probability of a given deviation from our expectation also can be computed, provided that random sampling has been performed. The same kind of reasoning applies to the estimates calculated for our sample with regard to the population parameters. We take, for example, the arithmetic average from our sample as an estimate of the mean value for the population. Any difference, if it exists, between the sample average and the population mean is attributed to random fluctuations in the process of selection of the members included in the sample. We can calculate the probability of any value of this difference, provided the sample was randomly selected. If the deviation between the sample estimate and the population parameter cannot be explained by chance, the estimate is said to be biased. The design of the observation or experiment provides validity to the estimates and the fundamental statistical paradigm is that of random sampling.

causal questions; 4.6 Descriptive vs

The first meaning of the word statistics relates to any summary quantity computed on a set of values. Descriptive indices or statistics such as the arithmetic average, the median or the mode, are widely used to summarize the information in a series of observations. Historically, these summary descriptors were used for administrative purposes by states, and therefore they were named statistics. In epidemiology, statistics that are commonly seen derive from the comparisons inherent to the nature of epidemiology, which asks questions such as: “Is one population at greater risk of disease than another?” In making such comparisons, the relative risk is a popular measure of the strength of association between an individual characteristic and the probability of becoming ill, and it is most commonly applied in aetiological research; attributable risk is also a measure of association between individual characteristics and disease occurrence, but it emphasizes the gain in terms of number of cases spared by an intervention which removes the factor in question—it is mostly applied in public health and preventive medicine.

Exploratory, Descriptive, and Causal Research Designs …

In case-control studies, other options exist to avoid biases. Interviewers, study staff and study participants need not be aware of the precise hypothesis under study. If they do not know the association being tested, they are less likely to try to provide the expected answer. Keeping study personnel in the dark as to the research hypothesis is in fact often very impractical. The interviewer will almost always know the exposures of greatest potential interest as well as who is a case and who is a control. We therefore have to rely on their honesty and also on their training in basic research methodology, which should be a part of their professional background; objectivity is the hallmark at all stages in science.

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