Hypothesis Testing With Means Lecture Outline (02:04)
In this video lecture, Professor Naomi Lowe will explain hypothesis testing with means of samples, discussing the distribution of means, the process of hypothesis testing with sample means, confidence intervals, and the creation of a confidence interval.
Distribution of Means (03:09)
The distribution of means is the spread of the sample means. The sample size of a population impacts the distribution of sample means. Lowe introduces an activity related to distribution of means.
Activity: Distribution of Means (09:49)
Lowe introduces an activity related to distribution of means and provides detailed instructions. As N increases, the spread of the distribution decreases.
Distribution of Means: Characteristics (05:02)
The mean of means is equal to the population mean. Central limit theorem indicates that the distribution of means will become normal when the sample size if over 30. Standard error bars are used in statistical research articles.
Process of Hypothesis Testing (04:56)
The steps for hypothesis testing include defining hypotheses, establishing a comparison distribution, determining a Z-cutoff score, and calculating a Z-score from the sample mean. Compare the calculate Z-score to the cut off area to determine if the null hypothesis is supported or rejected.
Confidence Intervals (09:18)
Confidence intervals are used to estimate the population mean based on the sample. Confidence intervals are 95 percent of 99 percent. Lowe provides examples of calculating confidence intervals.
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