Mean, Median & Mode: Measures of Central Tendency. If a data set is in the presence of some outliers, then the sample average could not be a "fair" measure of the centrality of that data set. Be guided by the advantages and disadvantages of each measure. DISADVANTAGES. The range is mostly used as a measure of dispersion with the mode and median Advantages: Easy to calculate; Takes into consideration extreme score; Disadvantages: Only using two scores in the data set and ignoring the rest; The extreme It is rigidly defined. Secondly, it is resistant to extreme values or outliers and is immune to sharp peaks in the data. When you determine the level of measurement of your variable of interest and whether or not there is skewness and/or extreme scores in your data set then you can determine the most appropriate measure of central tendency, as follows: Data measured at the nominal level: Of the three measures of central tendency examined in this chapter, the mode is the only appropriate one as the scores cannot be ordered from smallest to largest in a meaningful way. The mean is usually preferable. To determine the shape of a data set more accurately, you would need to look at other values, such as the median, range, and quartiles. Weighing up the advantages and disadvantages of each measure leads you to the following conclusion: the most appropriate measure of central tendency for a variable depends on the level of measurement of the variable and the nature of the distribution of scores within that variable. To illustrate, if a data set is comprised of five values that are all the same, the median value will still be that one value, making it look like the entire set is uniform. It is a useful tool for comparing two or more datasets, as it can determine if the data follows the same pattern. The scores can be ordered from smallest to largest and this is meaningful, however they cannot be added up so the mean cannot be calculated. .recentcomments a{display:inline !important;padding:0 !important;margin:0 !important;} The median is usually preferable, because its more informative than the mode. But it is easily affected by any extreme value/outlier. In some distributions, the mode may not reflect thecentre of the distributionvery well. Evaluation (AO3) of the Mode as a Measure of Central Tendency. can be distorted be extreme scores. It is easy to compute and comprehend. a summary of your, Role play 2 Presenting the loan offer to Jennifer and Philip In this role play, you will move forward to the next stage of Jennifer and Philip's loan process. In this situation the median is usually best.

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When putting together the psychology statistics you need to report when youre describing a variable in a report, you need to know which of the three measures of central tendency the mode, median and mean you should use. .stm_titlebox { ELABOATION:This is a strength because,only considering the middle score means that any other scores (in particular, rogue/extreme scores) are ignored, this makes the median more representative of the whole data set and therefore, the median can be said to be an accurate measure of central tendency. var stm_ajaxurl = 'https://www.sancakpalas.com/wp-admin/admin-ajax.php'; The mode has an advantage over the median and the mean because it can be computed for both numerical and categorical (non-numerical) data. Although this measure is widely used, it has some disadvantages. For example, if two individuals have incomes of $100,000 and $2,000,000, the mean income between the two would be $1,050,000. 3) MODE (AO1)-This is the most common score/the score that appears the most in a set of raw data. When calculating the correlation between several variables, the Mean offers more insights into the data than the alternative measures of central tendency. The median is the middle value in a set of numbers, or the average of the two middle values if there is an even number of values. Looking at the Mean, the numbers are spread out around the same value, but the data might be spread out from small numbers too much larger ones.

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Weighing up the advantages and disadvantages of each measure leads you to the following conclusion: the most appropriate measure of central tendency for a variable depends on the level of measurement of the variable and the nature of the distribution of scores within that variable.

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