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# Inferential Statistics

Understanding t-tests and inferential statistics

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### Inferential Statistics

1. 1. Understanding Statistical inference
2. 2. Inference • The process of drawing conclusions about population parameters based on a sample taken from the population
3. 3. Key points when you draw an inference 1)A sample is likely to be a good representation of the population 2)There is an element of uncertainty as to how well the sample represents the population. 3)The way the sample is taken matters.
4. 4. t test
5. 5. T tests 1.One Sample t test 2.Independent Samples t test 3.Dependent Samples t test
6. 6. What is hypotheses? •In statistics, a hypothesis is a claim or statement about a property of a population
7. 7. State alpha • α = 0.05
8. 8. 3. Calculating the degree of freedom • n-1 = 30-1 = 29 In statistics, the number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary. The number of independent ways by which a dynamic system can move, without violating any constraint imposed on it, is called number of degrees of freedom.
9. 9. 4. State the decision rule
10. 10. If t is less than -2.0452 and greater than 2.0452 reject the null hypothesis.
11. 11. Reject Null Hypothesis
12. 12. Independent samples t test
13. 13. Pooled Variance Sum of Squares Variance
14. 14. • What Is the Sum of Squares? • Sum of squares is a statistical technique used in statistical analysis to determine the dispersion of data points. • What is pooled variance? • In statistics, pooled variance (also known as combined, composite, or overall variance) is a method for estimating variance of several different populations when the mean of each population may be different, but one may assume that the variance of each population is the same.