30 Differences Between Mac and PC People

From 2006 until 2009, Apple ran a series of memorable TV advertisements that anthropomorphized a PC and a Mac in the forms of John Hodgman and Justin Long. Hodgman, playing the part of a PC, was dressed in a business suit, wore glasses, donned a haircut from 1985, and only broke from his mostly dull personality to get excited about making spreadsheets.  Long, on the other hand, sporting a t-shirt, stylish jeans, and a quick-witted personality, portrayed Macs as the computing choice for cooler, more modern consumers.

The ads were a clever marketing technique by Apple to portray themselves as the more modern and innovative computing platform. But they also played on people's general stereotype that Mac users tend to be younger, hipper, and more tech-savvy than PC users. So we crunched the numbers at SameGrain to find out if and how Mac and PC users differ.

We found that some of the stereotypes appear to have some grain of truth - Mac users do indeed tend to be slightly younger, more liberal, and more artistic. But some of the other differences may surprise you. Check out the plot below for the full results of the analysis.

The analysis was done using hundreds of thousands of data-points about users' preferences, experiences, and demographics from SameGrain, a platform for meeting new people (primarily friends) based on what you have in common. Characteristics are listed from top to bottom by those most unique to PC users to those most unique to Mac users. The center black lines indicate how much more likely a PC/Mac user is to have that characteristic relative to a user of the other computer platform. For each characteristic, the width of the box shows the uncertainty of the likelihood (for those interested, it's the 99% confidence interval). The plot is interactive: hovering over each bar will display a popup that explicates the difference between Mac and PC users in that characteristic. For example, a PC user is 14% - 30% more likely than a PC user to have owned a Ford vehicle. The color of the bar indicates how confident we are that PC/Mac users are more likely than users of the other computer platform to have that characteristic (statistic-savvy readers will recognize this as the z-score).

It's (Mostly) All About the Benjamins

Education and Money

Many of the differences between Mac and PC users can be directly attributed to differences in the buying power of the users (or buying power as implied from education and spending habits). The data implies Mac users are more educated and affluent than PC users.

Mac users are approximately 20% more likely to have a bachelor's degree and 32% more likely to have a graduate degree than PC users. These percentages are based on polling a sample of the population (SameGrain has not collected data on every Mac or PC user), so there is an uncertainty to the measurements, which can be calculated. We can say with 99% confidence that Mac users are 12-28% and 18-45% more likely to have a bachelor's and graduate degree, respectively.

Education level is highly correlated with income, so it is a safe conclusion to say that Mac users are generally wealthier than PC users. The income disparity is also born out in differences in spending habits. Perhaps the most obvious example is that PC users are more likely to own domestic vehicles such as those made by Chevrolet, Ford, and Dodge, while Mac users are more likely to own foreign vehicles from BMW or Volkswagen.

There has been quite a bit of research done that suggests a correlation between a person's income and the quality of their diet and the amount of sleep they get each night, both of which are born out in the SameGrain data. Note that PC users are more likely to eat junk food and get less than 8 hours of sleep, while Mac users are more likely to eat gourmet meals and sleep for more than 8 hours. We see similar trends when comparing how users spend their disposable income; PC users are more likely to live paycheck to paycheck, while Mac users are more likely to save for a new home. This agrees with research that finds people at higher income levels are more likely to save than those in lower income brackets. This trend probably also explains why PC users are more likely to have more than 3 children - such households likely have less disposable income.


We find that Mac users tend to be slightly younger than PC users. Mac users are about 6% more likely to be under the age of 35. It's a small - but statistically significant - difference. Other differences that could be attributed to age include political affiliation (PC users are more likely to be conservative while Mac users are more likely to be liberal) and stage of career (Mac users are more likely to be just starting their career). 


Some of the differences were surprising. PC users are more likely to list Family Guy as one of their favorite TV shows, not pay attention to the news, like football, and like their meat prepared well done. Mac users are more likely to prefer Coca-Cola over Pepsi, play a musical instrument, make their bed most mornings, have a gym membership, and play tennis. Some of these characteristics may be related to disposable income. If Mac users are wealthier, they may have money to spend on gym memberships and musical instruments. The popularity of video games among PC users would seem to argue against this explanation, but PCs have long held an edge over Macs among gamers.

Among the strongest characteristics of a Mac user is an affinity for art. Mac users are 25% more likely to identify art as their favorite high school subject. In fact, we find that all users who majored in art in college use Macs.

Winning the Middle and Winning the Base

As we get closer to November 2016, and candidates vie for the ever-important undecided voters, presidential candidates will begin moderating their positions. That's Politics 101: play to the base during nomination process, but aim for the middle during the general election. Using data collected by SameGrain, I set out to explore which issues are more likely to win the middle, which issues win the base, and which issues the middle and the base most disagree on.

It's the Economy (and Education) Stupid!

The plot above shows the likelihood users are to be concerned about certain political issues. Longer bars indicate a higher likelihood to be concerned. The bars are further broken down by the political leaning of the users. Very conservative, conservative, moderate, liberal, and very liberal users are colored red, orange, gray, teal, and blue, respectively. Non-moderate bars are labeled with the percentage more or less likely that a user of that political leaning is to be concerned about the issue relative to a moderate. For example, if you hover over the red bar next to "Terrorism," you will see that very conservative users are 24% more likely to be concerned about terrorism than moderates. Very liberal users, on the other hand, are 45% less likely to be concerned about terrorism.

The issues are ordered top to bottom from most to least concerning to moderates. The top five issues concerning to moderates include the Educational System, the Economy, Terrorism, School Violence & Bullying, and Healthcare, so candidates who are trying to appeal to the middle may be wise to focus on those issues. Note that SameGrain is a social media platform with a majority Millennial user base, which may explain the relatively high importance of school violence and bullying, which would presumably be less important to older generations.

Moderates Agree More With Liberals

In general, conservatives tend to care about more issues than liberals and moderates. There are 18 issues that conservative or very conservative users care about more than moderates, while there are 11 issues that liberal or very liberal users care about more than moderates. In fact, there are 12 issues that conservatives care over 25% more about than moderates; there are only 6 such issues for liberals. One way to measure which political base is more similar to moderates is to calculate the Pearson Product-Moment Correlation Coefficient, R. The table below shows the R values between the level of issue concern of each political base and that of moderates.

Correlation coefficient for each political base. All values are nearly 1, indicating strong correlation between the level of concern over the issues between the political bases and moderates.

Correlation coefficient for each political base. All values are nearly 1, indicating strong correlation between the level of concern over the issues between the political bases and moderates.

An R value of 0 indicates no correlation, while a value of 1 indicates perfect correlation. Note that all values of R are close to 1, indicating that the level of concern for the issues is positively correlated between all the bases and moderates - i.e., in general, the political bases agree with moderates. However, the R values of conservatives are smaller than those of liberals, indicating that the views of moderates are more similar to those of liberals than conservatives. Note also that the R values of the more centrist bases (liberal and conservative) are larger than those of the extreme bases (very conservative and very liberal), indicating that moderates agree more with centrists than those at the political extremes. This is of course unsurprising, and is the reason candidates shift their politics to the middle following primaries.

Conservatives & Liberals: Differences and Similarities

The below plot shows the same data as the first, except with issues ordered from top to bottom by most concerning to conservatives (left panel) and most concerning to liberals (right panel). With this view, it is easier to compare the differences between the two political leanings. The top 5 issues concerning conservatives are the Economy, Terrorism, the Educational System, Freedom of Speech, and Healthcare, while the top 5 issues for liberals are the Education System, Gender Equality, Global Warming & the Environment, Healthcare, and the Economy.

While there is much overlap between the issues that concern both sides of the political spectrum, concern about several issues - terrorism, gender equality, global warming, and pollution in particular - is much stronger on one side than the other. While gender equality and global warming are in the top 3 issues for liberals, they don't even make the top 15 issues for conservatives. In fact, most issues show this pattern where one side is more concerned than moderates, while the other is less concerned. Two exceptions of note are gun control and abortion, where both political bases are much more concerned than moderates, although presumably for opposite reasons. These hot-button issues are disproportionately cared about by the bases relative to the concerns of the middle.

Counting Votes by Counting Tears

Privacy Note:  SameGrain is a privacy-focused social media platform where anonymity is supported and promoted. The data presented in this blog is anonymized, having any attribution to individual users removed.

If you saw the Oscar-nominated movie Room this year, there's a good chance you or someone around you spent the movie wiping away tears. On SameGrain (available on the Apple app store), when asked if they cry during sad movies, nearly 2/3 of users said they do. In the interactive plot below, you can see the percentage of users who said they cry a lot, cry a little, control their emotions, or feel nothing when watching sad movies. 

You can also change the plot to see how users' answers depend on the candidate they support in the US presidential election. As the plots show, how much you cry during movies can be an indicator of how you lean politically. True to the bleeding-heart liberal stereotype, users voting for the Democratic candidates are about 1.3 times more likely to cry (or admit they cry) during a movie than those voting for a Republican candidate. Clinton and Sanders have the highest percentage of criers, at 69% and 68%, respectively. By contrast, 52%, 49%, and 59% of Trump, Cruz, and Rubio voters are criers.

Mixing Work & Politics

Privacy Note:  SameGrain is a privacy-focused social media platform where anonymity is supported and promoted. The data presented in this blog is anonymized, having any attribution to individual users removed.

We've all heard that you shouldn't talk politics at work. But with a 2016 election cycle that has already seen its share of headlines, it may be hard to avoid. For this blog post, I investigated the intersection between our political and professional lives, and as it turns out, what you do for work can be a good indicator of who you vote for.

Teachers Like Clinton, Police Like Trump

SameGrain (available in the Apple App Store) introduces users to people who are like them in millions of potential ways, including their political affiliation and profession. We asked SameGrain users who they were considering voting for in the 2016 US presidential election, and compared users' choice for president to their professions. The results are found in the interactive plot below. Results are limited to the current five leading presidential candidates: Clinton, Sanders, Trump, Cruz, and Rubio.

For each profession, the length of the bar indicates how likely a supporter of the candidate is to have that profession, relative to the average user. For example, if you set the plot to Bernie Sanders, you will see that his voters are 1.7 times more likely to work for non-profit, volunteer, or social service organizations compared to the average user. You can compare two candidates by using the drop-downs above each panel.

Unsurprisingly, Sanders and Clinton (both Democrats), have similar demographics. They both receive higher than average support from the visual art, education, entertainment, fashion, technology, publishing, non-profit, and recreation industries. Clinton enjoys significantly more support from those in government, finance, and law than Sanders, while Sanders captures slightly more students. With a combined 63% of profession categories having average or better support, the Democratic candidates can claim relatively wide support across many professions.

The Republican candidates' (Trump, Cruz, and Rubio) voters are dominated by executives, law enforcement, public safety, the military, and transportation professionals. Perhaps due to his experience as a building developer, Trump uniquely enjoys above average support from construction industry professionals. The largest differences in support between the political parties is with law enforcement and members of the military. The average voter for the Republican candidates is approximately 4 times more likely than a Democratic voter to be in law enforcement, and approximately 3 times more likely to be in the military. We found Republican candidate support was not as wide across the professions as Democratic support; 51% of professions had average or better support for the Republican candidates (recall Democrats had 63%).

Measuring Controversy

While Trump has the highest support of any single profession (Trump's relative support from construction and law enforcement lead all other candidate-profession pairs), his support from educators and teachers ranks as the lowest support of a candidate from any single profession. Indeed, Trump's controversial and divisive reputation is born out in the data. One measure of a candidate's divisiveness is the standard deviation of relative support amongst all professions, which essentially determines how well all professions agree. The grey band in the plot shows the standard deviation of each candidate; narrow bands indicate agreement among professions, while wider bands indicate more disagreement. Trump and Cruz have the largest standard deviation, and are thus considered more divisive by this measure.

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The Genealogy of Music

Privacy Note:  SameGrain is a privacy-focused social media platform where anonymity is supported and promoted. The data presented in this blog is anonymized, having any attribution to individual users removed.

I recently came across an interesting infographic by Reebee Garofalo titled “The Genealogy of Pop/Rock Music” that shows the origins of the multitude of popular music subgenres born between 1955 and 1978. You can order the book Visual Explanations by Edward Tufte, which published the graphic, here or see it in digital form here. If you zoom in, you can see the birth of surf music in the early 1960’s (and its death about 5 years later) and the splitting of pop folk into folk rock, country rock, and soft rock.

It’s a nice graphic that gives context and history to the similarities between songs and musical genres. However, the groupings of the various genres do not appear to have been data-driven, but are rather a product of author discretion. And so I decided to mine the SameGrain data to see what could be learned about the similarities and origins of music genres without applying any preconceived thoughts or notions.


SameGrain (currently available in the Apple App Store) introduces users to other users that are like them in millions of potential ways, including their music tastes. We asked our users to identify their favorite genres of music from the following 26 choices: blues, classical, classic rock, country, dance, disco, electric dance music, electronic, folk, funk, gospel, hard rock, hip-hop, indie, jazz, Latin, metal opera, pop rock, rap, reggae, relaxation, soul, talk, urban, and world music. The completeness of this list is beyond the scope of this blog post.

Users’ answers to this question provide thousands of anonymized data points with which I can do a cluster analysis; in other words, using the data we can group genres that are similar to each other. I make the assumption that if a user identifies two genres as favorites, the two genres are similar in some way – be it instrumentally, tonally, whatever. This of course is not always a perfect assumption. I like both classical and rock music, but you’d have to go several hundred years into the past to find their common musical ancestor. But aggregated over thousands of responses, this assumption is – to first order – safe.

I used the Pearson product-moment correlation coefficient to measure the similarity between each genre, and then hierarchically clustered the genres into groups by continuously merging the two most similar groups. The colorful figure with the circles is a graphical representation of that analysis. The circles are labeled by the musical genre they represent, and the circles’ positions in the plot identify the groupings – genres closer together are correlated with each other. For example, users who chose hard rock were more likely to also choose classic rock than those who didn’t. Therefore hard rock and classic rock are correlated in the data. Other correlated pairs include reggae and Latin, metal and hard rock, and funk and disco. Rap, pop rock, and hip-hop form a correlated trio. Genres like country and gospel are far away from other genres; while they are mildly correlated with each other, there is very little correlation between country and any other genre. In other words, a user’s preference for country music is not predictive of whether or not they like jazz or dance or any other genre other than gospel.

Genre Popularity

The size of the circles indicates the genre’s overall popularity. Pop rock is the most popular, with classic rock, indie (somewhat ironically), hip-hop, and country following close behind. Recall that country music is not well correlated with other genres, and yet it is fairly popular. That means it is popular with people of all music tastes; whether they like hip-hop, classic rock, indie, or the blues, they’re equally as likely to like country.

Popularity by Age

Finally, the color of the circle is indicative of the popularity of the genre with age group. If a genre is more popular with users under 30, it is more green in color, and if the genre is more popular with users over 30, it is blue. Rap is the genre that stands out most distinctly as being more popular to younger people, but the color stretch can be deceiving (I didn’t provide a color scale for this plot, sorry). In fact, a “young” person is only 5 times more likely to choose rap as a favorite genre than an “old” person (as a 31-year-old, I take a small offense to my own terminology of anyone over 30 being “old”). And while classical is so often identified as a genre for the “old,” I find that it is only marginally more popular to older generations. That said, the coloring of the dots does provide some historical information of the music genres. If you assume that the age of the generation that prefers the music is related to the age of the genre itself (not a terrible assumption), then rap, hip-hop, and indie are the newest genres of music. And since humans have been speaking for least a couple hundred thousand years, perhaps it’s no surprise that talk is the “oldest” genre.


While this style of plot is aesthetically pleasing and allows for a high density of information (circles can have position, size, and color), if you were only interested in the clustering, it’s better to represent this in a dendrogram. Before joining SameGrain, I was a researcher in the field of astrophysics, where dendrograms have become a popular way to illustrate the clustering of galaxies and the fragmentation of star-forming molecular clouds. The second plot is a dendrogram of the genres, where the genres most correlated with each other are connected by the smallest number of junctions, and the horizontal length of the connecting lines is (roughly) proportional to the correlation coefficient. In this plot, it is very easy to see groups of genres, clusters of groups, and clusters of clusters.

This plot is very reminiscent in form to that of a family tree, and while this analogy works well for some genres (e.g., metal and hard rock are children of classic rock), the parent-child analogy breaks down quickly if you go very far up the tree. However, the analogy works better if we consider linked genres to be cousins to each other, much in the way modern humans are cousins – not descendants – to chimpanzees. Reggae is related to Latin, which is related to dance, which is in turn related to electric dance and electronic music.

These two plots, which combine hierarchical clustering with some basic analysis techniques, led to some interesting insights. Most notable to me was the universality of many genres. Pop rock is by far the most popular genre overall and with both age groups (as its name would imply). Pop’s closest cousins, rap and hip-hop, tend to be preferred by younger generations, but are still popular to those over 30 at a non-negligible level (perhaps owing to the fact that the first rap song, “Rapper’s Delight,” is now over 30 years old). Country is slightly preferred by younger generations relative to older ones, and enjoys modest popularity from a diverse population of music tastes.

You can anonymously find people just like you – including those with the same music taste – on SameGrain, which can be downloaded from the Apple App Store. The app will match you to other users on over 45 million different topics, including all of Wikipedia. So find out what you have in common with your friends, and find new friends just like you on SameGrain!