Presentation on theme: "Statistical Analysis How do we make sense of the data we collect during a study or an experiment?"— Presentation transcript:
1 Statistical AnalysisHow do we make sense of the data we collect during a study or an experiment?
2 Two Kinds of Statistical Analysis Descriptive StatisticsOrganize and summarize dataInferential StatisticsInterpret data and draw conclusionsUsed to test validity of hypothesis
3 Descriptive Statistics Numbers that summarize a set of research data obtained from a sampleOrganized into a frequency distribution (orderly arrangement of scores)Can be pictured as a histogram (bar graph)Can be pictured as a frequency polygon (line graph that replaces the bars with single points and connects the points with a line)
4 Measures of Central Tendency Describe the average or most typical scores for a set of research dataMode – the most frequently occurring scoreBimodal – if two scores appear most frequentlyMultimodal – if three or more scores appear most frequentlyMedian – the middle score of the set ofMean – the average of the set of scores (most commonly used)
5 Activity: Dice and the Bell Curve Find a partner to work withOne person will roll the dice, the other will record the number rolledRoll the dice and add the numbers together and record the results.Roll the dice for as many times as you can for approximately minutes.
6 Organize your data How can you organize your data? First, create a frequency distribution of your numbersNext, graph the distribution using the x axis for the score and the y axis for the frequency.Finally, calculate the mean, median and mode for your data.
7 Normal Distribution (also called normal curve or bell-curve) A “normal distribution” of data means that most of the examples in a set of data are close to the mean (average), while relatively few example tend to one extreme or the other.Scores are often normally distributed. When this happens, the mode, median, and mean are all the same (in this case, 100).
8 Measures of Central Tendency in Dunder Mifflin Salaries Let’s look at the salaries of the employees of the Dunder Mifflin Paper Company in Scranton:$25,000-Pam$25,000- Kevin$25,000- Angela$100,000- Andy$100,000- Dwight$200,000- Jim$300,000- MichaelThe median salary looks good at $100,000.The mean salary also looks good at about $110,000.But the mode salary is only $25,000.Maybe not the best place to work.Then again, living in Scranton is kind of cheap.
9 Skewed DistributionsWhen a few extreme scores (called outliers) significantly affect the mean.Distributions where most of the scores are squeezed into one end are skewed. In very skewed distributions, the median is a better measure of central tendency than the mean.
10 SkewsA few of the scores stretch out away from the group like a tail. The skew is named for the direction of the tail.Tail going to the left – negatively skewedTail going to the right – positively skewed
11 Look at the above figure and note that when a variable is normally distributed, the mean, median, and mode are the same number.You can use the following two rules to provide some information about skewness even when you cannot see a line graph of the data (i.e., all you need is the mean and the median):1. Rule One. If the mean is less than the median, the data are skewed to the left.2. Rule Two. If the mean is greater than the median, the data are skewed to the right.
13 Measures of Variability Variability describes the spread or diversity of scores for a set of data.Range – The largest score minus the smallest scoreVariance and standard deviation – indicate the degree to which scores differ from each other. The higher the variance or SD, the more spread out the distribution is.
14 More on Standard Deviation Standard deviation is kind of the “mean of the mean” and can often help you get the real story behind the data. It is how far, on average, scores deviate from the mean.The standard deviation is a statistic that tells you how tightly all the various examples are clustered around the mean in a set of data. When the examples are pretty tightly bunched together and the bell-shaped curve is steep, the standard deviation is small. When the examples are spread apart and the bell curve is relatively flat, that tells you that you have a relatively large standard deviation.
15 One standard deviation away from the mean in either direction on the horizontal axis (the red area on the graph) accounts for around 68% of the people in this group. Two standard deviations away from the mean (the red and green areas) account for roughly 95% of the people. And three standard deviations (the red, green and blue areas) account for about 99% of the people.If this curve were flatter and more spread out, the standard deviation would have to be larger in order to account for those 68% or so of the people. So that's why the standard deviation can tell you how spread out the examples in a set are from the mean.
16 To Calculate VarianceTo calculate the variance for the set of numbers 4, 5, 5, 6, 6, 6, 6, 7, 7, 8:Calculate the mean (average) – 60÷10 = 6Subtract the mean from each score in the distribution aboveThis shows you how far each score deviates from the mean, and when you add all of these numbers together, they should always equal zero.4-6=-25-6=-16-6=7-6=18-6=2
17 To Calculate Variance (cont.) However, we want to convert the scores to a form that allows us to add them up and not get zero. Therefore, we square all of the deviations scores, which removes all of the negative values.Now when we add them up, we get The larger this number is, the greater the dispersion of the scores is.Now divide the sum above by the number of scores in the group. This gives you an estimate of the average distance that a score is away from the mean.12 ÷ 10 = 1.2-2 (square this)-2 x -2=4-1-1 x -1=10 x 0=1 x 1=22 x 2=
18 To Calculate Standard Deviation To calculate standard deviation, all you do is calculate the square root of the variation you just calculated.√1.2 = 1.1The smaller this number is, the more confident you can be in using the mean to represent the group.
19 Descriptive Statistics PsychSim Homework:Descriptive Statistics worksheet (under documents on my website)tive%20Statistics/PsychSim_Shell.html
20 Try it yourself1, 3, 5, 5, 6, 7, 7, 8, 9, 9For this set of data, calculateMedianModeMeanRangeVarianceStandard Deviation
22 Inferential Statistics Whereas descriptive statistics simply summarize data, inferential statistics attempt to make inferences about a larger population based on the data set.They help determine whether or not the results of the study apply to the larger population from which the sample was taken.
23 Inferential Statistics (cont.) Any time you collect data, it will contain variability due to chance. For example, by chance alone, you might collect data from more freshman than from sophomores. If you repeated your data collection several times, you would get somewhat different results each time due to this chance variability.If this chance variability always exists in data collection, how can a researcher be confident that the inferences he or she makes about the larger population (the entire Shorecrest student body) is accurate? We use inferential statistics! Instead of making absolute conclusions about the population, researchers make statements about the population using the laws of probability and statistical significance.
24 Statistical Significance (sometimes known as a p score or p value) When inferential statistics demonstrate a high probability that research results are not due to chance, the results are said to be statistically significant.Psychologists say that something is statistically significant when the probability that it might be due to chance is less than 5 in 100 (indicated by the notation p < .05)In other words, there is less than a 5% chance that your results occurred just coincidentally, or by chance.
25 More about the p value….The smaller the p value, the greater the significanceWhy can a p value never equal 0?The p value can also be computed for any correlation coefficent which will indicate the strength of a relationship.
27 To Summarize Descriptive Statistics Organize and summarize data Central Tendency: mean, median, modeStandard deviation: variation in dataRange: distance from smallest to largestInferential StatisticsInterpret data and draw conclusionsUsed to test validity of hypothesis
28 Critical Thinking with Statistics The old saying goes”… there are three kinds of lies – lies, damned lies, and statistics.”The presentation of research findings in the form of numbers, graphs, etc., may look impressive, but remember that they can be distorted to make you believe something that is not necessarily true.The next slide shows some common ways in which this is done.
29 Biased or insufficient samples “Four out of five dentists surveyed recommended Brand X gum.”The # of dentists surveyed is not clearHow were the dentists chosen? Was it a random sample, or were 5 dentists chosen because they hold stock in Brand X gum?Many mail-in surveys suffer from a selection bias – the people who send them in may differ in important ways from those who do not.
30 The Misleading Average Example: The principal of a small private school met the criticism that his faculty has no teaching experience by issuing the statement that the average experience of each member of the faculty was 5 years.This statement was technically true: there were five teachers in the school including the principal, but the principal neglected to mention that he had twenty-five years of experience while the remaining four members had none.