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What does it mean for the finding of a statistical analysis of data to be statistically significant?
Data is statistically significant if the p (probability) value is below a certain level (ex: 5% or 1%). The p value describes how often one would receive the results they got if left to chance alone. The lower the p value, the less likely it is that your results were due to chance and is stronger evidence against the null hypothesis. Also important to keep in mind is that just because something is statistically significant does not mean it is practically significant.
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Is .001 in statics from the Independent Samples t Test analysis a significant finding?
In the context of an Independent Samples t-Test, a p-value of .001 indicates a statistically significant finding, meaning there is strong evidence to reject the null hypothesis. This suggests that the difference in means between the two groups being compared is unlikely to have occurred by chance. Typically, a p-value below .05 is considered significant, so .001 is well below this threshold.
How do you explain the analysis of variance assuming that your audience has not had a statistic class before?
The measurement of any statistical variable will vary from one observation to another. Some of this variation is systematic - due to variations in some other variable that "explains" these variations. There may be several such explanatory variables - acting in isolation or in conjunction with one another. Finally, there will be a residual variation which cannot be explained by any of these "explanatory" variables. The statistical technique called analysis of variance first calculates the total variation in the observations. The next step is to calculate what proportion of that variation can be "explained" by other variables, and finding the residual variation. A comparison of the explained variation with the residual variation is an indicator of whether or not the amount explained is statistically significant. The word "explain" is in quotes because there is not always a causal relationship. The causality may go in the opposite direction. Or the variables may be related to another variable that is not part of the analysis.
What does major finding mean?
A major finding refers to a significant result or discovery that emerges from research or analysis, which can have important implications for a particular field or subject. It typically highlights new insights, patterns, or conclusions that contribute to the understanding of a topic. Major findings often lead to further investigation, influence policy or practice, and may challenge existing theories or beliefs.
How do you find mid point when class interval and ffrequency is given?
To find the midpoint of a class interval, you add the lower limit and the upper limit of the interval and then divide the sum by 2. For example, if the class interval is 10-20, the midpoint would be (10 + 20) / 2 = 15. This midpoint can then be used in calculations like finding the mean or in statistical analysis involving frequency distributions.
What was the most significant contribution of pi to the field of mathematics?
it has helped in finding the perimeters and areas of circle.
What is the difference between statistical significance versus practical significance?
Statistical significance means that you are sure that the statistic is reliable. It is very possible that whatever you conclusion or finding is, it may not be important or it not have any decision-making utility. For example, my diet program has a 1 oz weight loss per month and I can show that is statistically significant. Do you really want a diet like that? It is not practically significant
Is .001 in statics from the Independent Samples t Test analysis a significant finding?
In the context of an Independent Samples t-Test, a p-value of .001 indicates a statistically significant finding, meaning there is strong evidence to reject the null hypothesis. This suggests that the difference in means between the two groups being compared is unlikely to have occurred by chance. Typically, a p-value below .05 is considered significant, so .001 is well below this threshold.
How do you explain the analysis of variance assuming that your audience has not had a statistic class before?
The measurement of any statistical variable will vary from one observation to another. Some of this variation is systematic - due to variations in some other variable that "explains" these variations. There may be several such explanatory variables - acting in isolation or in conjunction with one another. Finally, there will be a residual variation which cannot be explained by any of these "explanatory" variables. The statistical technique called analysis of variance first calculates the total variation in the observations. The next step is to calculate what proportion of that variation can be "explained" by other variables, and finding the residual variation. A comparison of the explained variation with the residual variation is an indicator of whether or not the amount explained is statistically significant. The word "explain" is in quotes because there is not always a causal relationship. The causality may go in the opposite direction. Or the variables may be related to another variable that is not part of the analysis.
What has the author A Hood Roberts written?
A Hood Roberts has written: 'A statistical linguistic analysis of American English'
What are examples of practical significance?
Practical significance refers to the real-world relevance or importance of a finding, beyond mere statistical significance. For example, a new medication may show a statistically significant reduction in symptoms, but if the actual improvement is minimal and doesn't enhance quality of life, it may lack practical significance. Similarly, a study might find that a training program improves employee productivity by 1%, which, while statistically significant, may not be meaningful enough to justify its cost. Thus, practical significance helps determine if results can lead to actionable decisions or improvements in practice.
Why is the standard normal distribution important in statistical analysis?
Why is normal distribution important in statistical analysis? Why is normal distribution important in statistical analysis? An important statistical effect was named for this manufacturing plant. What is it? In a famous research study conducted in the years 1927-1932 at an electrical equipment manufacturing plant, experimenters measured the influence of a number of variables (brightness of lights, temperature, group pressure, working hours, and managerial leadership) on the productivity of the employees. The major finding of the study was that no matter what experimental treatment was employed, the production of the workers seemed to improve. It seemed as though just knowing that they were being studied had a strong positive influence on the workers. .The Hawthorne effect
What is actual use of integration in life?
Statistically analysis. Finding areas under the curves of normal distributions. Look in the back of any statistics text at the t scores. All done for you by calculus. Statistics are vital in many walks of life, from business to science.
What are the approaches in Quantitative techniques?
Quantitative techniques primarily include statistical analysis, mathematical modeling, and optimization methods. Statistical analysis involves the use of data to identify patterns, relationships, and trends, while mathematical modeling formulates real-world problems into mathematical expressions for analysis. Optimization techniques focus on finding the best solution from a set of feasible options, often using algorithms and simulations. Together, these approaches facilitate informed decision-making in various fields such as finance, marketing, and operations management.
How do you find sxx and sxy in finding the line of regression?
Actually, this would require a graphic illustrations and various complex mathematical formulas to adequately explain the whole process. Any statistical analysis text could do this job quite well.
What is the significance of finding a contiguous subarray in the context of algorithmic complexity analysis?
Finding a contiguous subarray is significant in algorithmic complexity analysis because it helps in determining the efficiency of algorithms in terms of time and space. By analyzing the performance of algorithms on subarrays, we can understand how they scale with input size and make informed decisions about their efficiency.
What has the author Leonard H Zacks written?
Leonard H. Zacks has written: 'Idle time in a parallel channel queue' -- subject(s): System analysis, Queuing theory 'Queueing theoretic analysis of contractors' sequential bidding problems' -- subject(s): Queuing theory
Is 0.099 significant in a p-value of 0.05?
Yes, a p-value of 0.099 is greater than the significance level of 0.05. This indicates that the result is not statistically significant, meaning there is insufficient evidence to reject the null hypothesis at that level of significance. Therefore, the finding may not be considered strong enough to draw definitive conclusions.
