1000 Lots Of Happiness In The Game 1.1 serial key or number
1000 Lots Of Happiness In The Game 1.1 serial key or number
Answering questions with data
I have studied many languages-French, Spanish and a little Italian, but no one told me that Statistics was a foreign language. —Charmaine J. Forde
Sections & - Adapted text by Danielle Navarro Section - & - Mix of Matthew Crump & Danielle Navarro Section - Adapted text by Danielle Navarro
Up to this point in the book, we’ve discussed some of the key ideas in experimental design, and we’ve talked a little about how you can summarize a data set. To a lot of people, this is all there is to statistics: it’s about calculating averages, collecting all the numbers, drawing pictures, and putting them all in a report somewhere. Kind of like stamp collecting, but with numbers. However, statistics covers much more than that. In fact, descriptive statistics is one of the smallest parts of statistics, and one of the least powerful. The bigger and more useful part of statistics is that it provides tools that let you make inferences about data.
Once you start thinking about statistics in these terms – that statistics is there to help us draw inferences from data – you start seeing examples of it everywhere. For instance, here’s a tiny extract from a newspaper article in the Sydney Morning Herald (30 Oct ):
“I have a tough job,” the Premier said in response to a poll which found her government is now the most unpopular Labor administration in polling history, with a primary vote of just 23 per cent.
This kind of remark is entirely unremarkable in the papers or in everyday life, but let’s have a think about what it entails. A polling company has conducted a survey, usually a pretty big one because they can afford it. I’m too lazy to track down the original survey, so let’s just imagine that they called voters at random, and (23%) of those claimed that they intended to vote for the party. For the Federal election, the Australian Electoral Commission reported 4,, enrolled voters in New South Whales; so the opinions of the remaining 4,, voters (about % of voters) remain unknown to us. Even assuming that no-one lied to the polling company the only thing we can say with % confidence is that the true primary vote is somewhere between / (about %) and / (about %). So, on what basis is it legitimate for the polling company, the newspaper, and the readership to conclude that the ALP primary vote is only about 23%?
The answer to the question is pretty obvious: if I call people at random, and of them say they intend to vote for the ALP, then it seems very unlikely that these are the only people out of the entire voting public who actually intend to do so. In other words, we assume that the data collected by the polling company is pretty representative of the population at large. But how representative? Would we be surprised to discover that the true ALP primary vote is actually 24%? 29%? 37%? At this point everyday intuition starts to break down a bit. No-one would be surprised by 24%, and everybody would be surprised by 37%, but it’s a bit hard to say whether 29% is plausible. We need some more powerful tools than just looking at the numbers and guessing.
Inferential statistics provides the tools that we need to answer these sorts of questions, and since these kinds of questions lie at the heart of the scientific enterprise, they take up the lions share of every introductory course on statistics and research methods. However, our tools for making statistical inferences are 1) built on top of probability theory, and 2) require an understanding of how samples behave when you take them from distributions (defined by probability theory…). So, this chapter has two main parts. A brief introduction to probability theory, and an introduction to sampling from distributions.
How are probability and statistics different?
Before we start talking about probability theory, it’s helpful to spend a moment thinking about the relationship between probability and statistics. The two disciplines are closely related but they’re not identical. Probability theory is “the doctrine of chances”. It’s a branch of mathematics that tells you how often different kinds of events will happen. For example, all of these questions are things you can answer using probability theory:
What are the chances of a fair coin coming up heads 10 times in a row?
If I roll two six sided dice, how likely is it that I’ll roll two sixes?
How likely is it that five cards drawn from a perfectly shuffled deck will all be hearts?
What are the chances that I’ll win the lottery?
Notice that all of these questions have something in common. In each case the “truth of the world” is known, and my question relates to the “what kind of events” will happen. In the first question I know that the coin is fair, so there’s a 50% chance that any individual coin flip will come up heads. In the second question, I know that the chance of rolling a 6 on a single die is 1 in 6. In the third question I know that the deck is shuffled properly. And in the fourth question, I know that the lottery follows specific rules. You get the idea. The critical point is that probabilistic questions start with a known model of the world, and we use that model to do some calculations.
The underlying model can be quite simple. For instance, in the coin flipping example, we can write down the model like this: \(P(\mbox{heads}) = \) which you can read as “the probability of heads is ”.
As we’ll see later, in the same way that percentages are numbers that range from 0% to %, probabilities are just numbers that range from 0 to 1. When using this probability model to answer the first question, I don’t actually know exactly what’s going to happen. Maybe I’ll get 10 heads, like the question says. But maybe I’ll get three heads. That’s the key thing: in probability theory, the model is known, but the data are not.
So that’s probability. What about statistics? Statistical questions work the other way around. In statistics, we know the truth about the world. All we have is the data, and it is from the data that we want to learn the truth about the world. Statistical questions tend to look more like these:
If my friend flips a coin 10 times and gets 10 heads, are they playing a trick on me?
If five cards off the top of the deck are all hearts, how likely is it that the deck was shuffled?
If the lottery commissioner’s spouse wins the lottery, how likely is it that the lottery was rigged?
This time around, the only thing we have are data. What I know is that I saw my friend flip the coin 10 times and it came up heads every time. And what I want to infer is whether or not I should conclude that what I just saw was actually a fair coin being flipped 10 times in a row, or whether I should suspect that my friend is playing a trick on me. The data I have look like this:
and what I’m trying to do is work out which “model of the world” I should put my trust in. If the coin is fair, then the model I should adopt is one that says that the probability of heads is ; that is, \(P(\mbox{heads}) = \). If the coin is not fair, then I should conclude that the probability of heads is not , which we would write as \(P(\mbox{heads}) \neq \). In other words, the statistical inference problem is to figure out which of these probability models is right. Clearly, the statistical question isn’t the same as the probability question, but they’re deeply connected to one another. Because of this, a good introduction to statistical theory will start with a discussion of what probability is and how it works.
What does probability mean?
Let’s start with the first of these questions. What is “probability”? It might seem surprising to you, but while statisticians and mathematicians (mostly) agree on what the rules of probability are, there’s much less of a consensus on what the word really means. It seems weird because we’re all very comfortable using words like “chance”, “likely”, “possible” and “probable”, and it doesn’t seem like it should be a very difficult question to answer. If you had to explain “probability” to a five year old, you could do a pretty good job. But if you’ve ever had that experience in real life, you might walk away from the conversation feeling like you didn’t quite get it right, and that (like many everyday concepts) it turns out that you don’t really know what it’s all about.
So I’ll have a go at it. Let’s suppose I want to bet on a soccer game between two teams of robots, Arduino Arsenal and C Milan. After thinking about it, I decide that there is an 80% probability that Arduino Arsenal winning. What do I mean by that? Here are three possibilities…
They’re robot teams, so I can make them play over and over again, and if I did that, Arduino Arsenal would win 8 out of every 10 games on average.
For any given game, I would only agree that betting on this game is only “fair” if a $1 bet on C Milan gives a $5 payoff (i.e. I get my $1 back plus a $4 reward for being correct), as would a $4 bet on Arduino Arsenal (i.e., my $4 bet plus a $1 reward).
My subjective “belief” or “confidence” in an Arduino Arsenal victory is four times as strong as my belief in a C Milan victory.
Each of these seems sensible. However they’re not identical, and not every statistician would endorse all of them. The reason is that there are different statistical ideologies (yes, really!) and depending on which one you subscribe to, you might say that some of those statements are meaningless or irrelevant. In this section, I give a brief introduction the two main approaches that exist in the literature. These are by no means the only approaches, but they’re the two big ones.
The frequentist view
The first of the two major approaches to probability, and the more dominant one in statistics, is referred to as the frequentist view, and it defines probability as a long-run frequency. Suppose we were to try flipping a fair coin, over and over again. By definition, this is a coin that has \(P(H) = \). What might we observe? One possibility is that the first 20 flips might look like this:
In this case 11 of these 20 coin flips (55%) came up heads. Now suppose that I’d been keeping a running tally of the number of heads (which I’ll call \(N_H\)) that I’ve seen, across the first \(N\) flips, and calculate the proportion of heads \(N_H / N\) every time. Here’s what I’d get (I did literally flip coins to produce this!):
| number of flips | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| number of heads | 0 | 1 | 2 | 3 | 4 | 4 | 4 | 5 | 6 | 7 |
| proportion |
| number of flips | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 |
| number of heads | 8 | 8 | 9 | 10 | 10 | 10 | 10 | 10 | 10 | 11 |
| proportion |
Notice that at the start of the sequence, the proportion of heads fluctuates wildly, starting at and rising as high as Later on, one gets the impression that it dampens out a bit, with more and more of the values actually being pretty close to the “right” answer of This is the frequentist definition of probability in a nutshell: flip a fair coin over and over again, and as \(N\) grows large (approaches infinity, denoted \(N\rightarrow \infty\)), the proportion of heads will converge to 50%. There are some subtle technicalities that the mathematicians care about, but qualitatively speaking, that’s how the frequentists define probability. Unfortunately, I don’t have an infinite number of coins, or the infinite patience required to flip a coin an infinite number of times. However, I do have a computer, and computers excel at mindless repetitive tasks. So I asked my computer to simulate flipping a coin times, and then drew a picture of what happens to the proportion \(N_H / N\) as \(N\) increases. Actually, I did it four times, just to make sure it wasn’t a fluke. The results are shown in Figure [fig:frequentistprobability]. As you can see, the proportion of observed heads eventually stops fluctuating, and settles down; when it does, the number at which it finally settles is the true probability of heads.
Figure An illustration of how frequentist probability works. If you flip a fair coin over and over again, the proportion of heads that you’ve seen eventually settles down, and converges to the true probability of Each panel shows four different simulated experiments: in each case, we pretend we flipped a coin times, and kept track of the proportion of flips that were heads as we went along. Although none of these sequences actually ended up with an exact value of .5, if we’d extended the experiment for an infinite number of coin flips they would have.
The frequentist definition of probability has some desirable characteristics. First, it is objective: the probability of an event is necessarily grounded in the world. The only way that probability statements can make sense is if they refer to (a sequence of) events that occur in the physical universe. Second, it is unambiguous: any two people watching the same sequence of events unfold, trying to calculate the probability of an event, must inevitably come up with the same answer.
However, it also has undesirable characteristics. Infinite sequences don’t exist in the physical world. Suppose you picked up a coin from your pocket and started to flip it. Every time it lands, it impacts on the ground. Each impact wears the coin down a bit; eventually, the coin will be destroyed. So, one might ask whether it really makes sense to pretend that an “infinite” sequence of coin flips is even a meaningful concept, or an objective one. We can’t say that an “infinite sequence” of events is a real thing in the physical universe, because the physical universe doesn’t allow infinite anything.
More seriously, the frequentist definition has a narrow scope. There are lots of things out there that human beings are happy to assign probability to in everyday language, but cannot (even in theory) be mapped onto a hypothetical sequence of events. For instance, if a meteorologist comes on TV and says, “the probability of rain in Adelaide on 2 November is 60%” we humans are happy to accept this. But it’s not clear how to define this in frequentist terms. There’s only one city of Adelaide, and only 2 November There’s no infinite sequence of events here, just a once-off thing. Frequentist probability genuinely forbids us from making probability statements about a single event. From the frequentist perspective, it will either rain tomorrow or it will not; there is no “probability” that attaches to a single non-repeatable event. Now, it should be said that there are some very clever tricks that frequentists can use to get around this. One possibility is that what the meteorologist means is something like this: “There is a category of days for which I predict a 60% chance of rain; if we look only across those days for which I make this prediction, then on 60% of those days it will actually rain”. It’s very weird and counterintuitive to think of it this way, but you do see frequentists do this sometimes.
The Bayesian view
The Bayesian view of probability is often called the subjectivist view, and it is a minority view among statisticians, but one that has been steadily gaining traction for the last several decades. There are many flavours of Bayesianism, making hard to say exactly what “the” Bayesian view is. The most common way of thinking about subjective probability is to define the probability of an event as the degree of belief that an intelligent and rational agent assigns to that truth of that event. From that perspective, probabilities don’t exist in the world, but rather in the thoughts and assumptions of people and other intelligent beings. However, in order for this approach to work, we need some way of operationalising “degree of belief”. One way that you can do this is to formalise it in terms of “rational gambling”, though there are many other ways. Suppose that I believe that there’s a 60% probability of rain tomorrow. If someone offers me a bet: if it rains tomorrow, then I win $5, but if it doesn’t rain then I lose $5. Clearly, from my perspective, this is a pretty good bet. On the other hand, if I think that the probability of rain is only 40%, then it’s a bad bet to take. Thus, we can operationalise the notion of a “subjective probability” in terms of what bets I’m willing to accept.
What are the advantages and disadvantages to the Bayesian approach? The main advantage is that it allows you to assign probabilities to any event you want to. You don’t need to be limited to those events that are repeatable. The main disadvantage (to many people) is that we can’t be purely objective – specifying a probability requires us to specify an entity that has the relevant degree of belief. This entity might be a human, an alien, a robot, or even a statistician, but there has to be an intelligent agent out there that believes in things. To many people this is uncomfortable: it seems to make probability arbitrary. While the Bayesian approach does require that the agent in question be rational (i.e., obey the rules of probability), it does allow everyone to have their own beliefs; I can believe the coin is fair and you don’t have to, even though we’re both rational. The frequentist view doesn’t allow any two observers to attribute different probabilities to the same event: when that happens, then at least one of them must be wrong. The Bayesian view does not prevent this from occurring. Two observers with different background knowledge can legitimately hold different beliefs about the same event. In short, where the frequentist view is sometimes considered to be too narrow (forbids lots of things that that we want to assign probabilities to), the Bayesian view is sometimes thought to be too broad (allows too many differences between observers).
What’s the difference? And who is right?
Now that you’ve seen each of these two views independently, it’s useful to make sure you can compare the two. Go back to the hypothetical robot soccer game at the start of the section. What do you think a frequentist and a Bayesian would say about these three statements? Which statement would a frequentist say is the correct definition of probability? Which one would a Bayesian do? Would some of these statements be meaningless to a frequentist or a Bayesian? If you’ve understood the two perspectives, you should have some sense of how to answer those questions.
Okay, assuming you understand the different, you might be wondering which of them is right? Honestly, I don’t know that there is a right answer. As far as I can tell there’s nothing mathematically incorrect about the way frequentists think about sequences of events, and there’s nothing mathematically incorrect about the way that Bayesians define the beliefs of a rational agent. In fact, when you dig down into the details, Bayesians and frequentists actually agree about a lot of things. Many frequentist methods lead to decisions that Bayesians agree a rational agent would make. Many Bayesian methods have very good frequentist properties.
For the most part, I’m a pragmatist so I’ll use any statistical method that I trust. As it turns out, that makes me prefer Bayesian methods, for reasons I’ll explain towards the end of the book, but I’m not fundamentally opposed to frequentist methods. Not everyone is quite so relaxed. For instance, consider Sir Ronald Fisher, one of the towering figures of 20th century statistics and a vehement opponent to all things Bayesian, whose paper on the mathematical foundations of statistics referred to Bayesian probability as “an impenetrable jungle [that] arrests progress towards precision of statistical concepts” Fisher (, ). Or the psychologist Paul Meehl, who suggests that relying on frequentist methods could turn you into “a potent but sterile intellectual rake who leaves in his merry path a long train of ravished maidens but no viable scientific offspring” Meehl (, ). The history of statistics, as you might gather, is not devoid of entertainment.
Basic probability theory
Ideological arguments between Bayesians and frequentists notwithstanding, it turns out that people mostly agree on the rules that probabilities should obey. There are lots of different ways of arriving at these rules. The most commonly used approach is based on the work of Andrey Kolmogorov, one of the great Soviet mathematicians of the 20th century. I won’t go into a lot of detail, but I’ll try to give you a bit of a sense of how it works. And in order to do so, I’m going to have to talk about my pants.
Introducing probability distributions
One of the disturbing truths about my life is that I only own 5 pairs of pants: three pairs of jeans, the bottom half of a suit, and a pair of tracksuit pants. Even sadder, I’ve given them names: I call them \(X_1\), \(X_2\), \(X_3\), \(X_4\) and \(X_5\). I really do: that’s why they call me Mister Imaginative. Now, on any given day, I pick out exactly one of pair of pants to wear. Not even I’m so stupid as to try to wear two pairs of pants, and thanks to years of training I never go outside without wearing pants anymore. If I were to describe this situation using the language of probability theory, I would refer to each pair of pants (i.e., each \(X\)) as an elementary event. The key characteristic of elementary events is that every time we make an observation (e.g., every time I put on a pair of pants), then the outcome will be one and only one of these events. Like I said, these days I always wear exactly one pair of pants, so my pants satisfy this constraint. Similarly, the set of all possible events is called a sample space. Granted, some people would call it a “wardrobe”, but that’s because they’re refusing to think about my pants in probabilistic terms. Sad.
Okay, now that we have a sample space (a wardrobe), which is built from lots of possible elementary events (pants), what we want to do is assign a probability of one of these elementary events. For an event \(X\), the probability of that event \(P(X)\) is a number that lies between 0 and 1. The bigger the value of \(P(X)\), the more likely the event is to occur. So, for example, if \(P(X) = 0\), it means the event \(X\) is impossible (i.e., I never wear those pants). On the other hand, if \(P(X) = 1\) it means that event \(X\) is certain to occur (i.e., I always wear those pants). For probability values in the middle, it means that I sometimes wear those pants. For instance, if \(P(X) = \) it means that I wear those pants half of the time.
At this point, we’re almost done. The last thing we need to recognise is that “something always happens”. Every time I put on pants, I really do end up wearing pants (crazy, right?). What this somewhat trite statement means, in probabilistic terms, is that the probabilities of the elementary events need to add up to 1. This is known as the law of total probability, not that any of us really care. More importantly, if these requirements are satisfied, then what we have is a probability distribution. For example, this is an example of a probability distribution
| Which pants? | Label | Probability |
|---|---|---|
| Blue jeans | \(X_1\) | \(P(X_1) = .5\) |
| Grey jeans | \(X_2\) | \(P(X_2) = .3\) |
| Black jeans | \(X_3\) | \(P(X_3) = .1\) |
| Black suit | \(X_4\) | \(P(X_4) = 0\) |
| Blue tracksuit | \(X_5\) | \(P(X_5) = .1\) |
Each of the events has a probability that lies between 0 and 1, and if we add up the probability of all events, they sum to 1. Awesome. We can even draw a nice bar graph to visualise this distribution, as shown in Figure And at this point, we’ve all achieved something. You’ve learned what a probability distribution is, and I’ve finally managed to find a way to create a graph that focuses entirely on my pants. Everyone wins!
Serial number
A serial number is a unique identifier assigned incrementally or sequentially to an item, to uniquely identify it.
Serial numbers need not be strictly numerical. They may contain letters and other typographical symbols, or may consist entirely of a characterstring.1
Applications of serial numbering[edit]
Serial numbers identify otherwise identical individual units with many, obvious uses. Serial numbers are a deterrent against theft and counterfeit products, as they can be recorded, and stolen or otherwise irregular goods can be identified. Some items with serial numbers are automobiles, electronics, and appliances. Banknotes and other transferable documents of value bear serial numbers to assist in preventing counterfeiting and tracing stolen ones.
They are valuable in quality control, as once a defect is found in the production of a particular batch of product, the serial number will identify which units are affected.
Serial numbers for intangible goods[edit]
Serial numbers may be used to identify individual physical or intangible objects (for example computer software or the right to play an online multiplayer game). The purpose and application is different. A software serial number, otherwise called product key, is usually not embedded in the software, but is assigned to a specific user with a right to use the software. The software will function only if a potential user enters a valid product code. The vast majority of possible codes are rejected by the software. If an unauthorised user is found to be using the software, the legitimate user can be identified from the code. It is usually not impossible, however, for an unauthorised user to create a valid but unallocated code either by trying many possible codes, or reverse engineering the software; use of unallocated codes can be monitored if the software makes an Internet connection.
Other uses of the term[edit]
The term serial number is sometimes used for codes which do not identify a single instance of something. For example, the International Standard Serial Number or ISSN used on magazines, journals and other periodicals, an equivalent to the International Standard Book Number (ISBN) applied to books, is assigned to each periodical. It takes its name from the library science use of the word serial to mean a periodical.
Certificates and certificate authorities (CA) are necessary for widespread use of cryptography. These depend on applying mathematically rigorous serial numbers and serial number arithmetic, again not identifying a single instance of the content being protected.
Military and government use[edit]
The term serial number is also used in military formations as an alternative to the expression service number.[citation needed] In air forces, the serial number is used to uniquely identify individual aircraft and is usually painted on both sides of the aircraft fuselage, most often in the tail area, although in some cases the serial is painted on the side of the aircraft's fin/rudder(s). Because of this, the serial number is sometimes called a tail number.
In the UK Royal Air Force (RAF) the individual serial takes the form of two letters followed by three digits, e.g., BT—the prototype Avro Lancaster, or XS—an English Electric Lightning F.6 at one time based at RAF Binbrook.[1] During the Second World War RAF aircraft that were secret or carrying secret equipment had "/G" (for "Guard") appended to the serial, denoting that the aircraft was to have an armed guard at all times while on the ground, e.g., LZ/G—the prototype de Havilland Vampirejetfighter, or ML/G—a de Havilland Mosquito XVI experimentally fitted with H2S radar. Prior to this scheme the RAF, and predecessor Royal Flying Corps (RFC), utilised a serial consisting of a letter followed by four figures, e.g., D—a Bristol F.2 Fighter currently owned by the Shuttleworth Collection, or K—the prototype Supermarine Spitfire. The serial number follows the aircraft throughout its period of service.
In , the U.S. FDA published draft guidance for the pharmaceutical industry to use serial numbers on prescription drug packages.[2] This measure is intended to enhance the traceability of drugs and to help prevent counterfeiting.
Serial number arithmetic[edit]
Serial numbers are often used in network protocols. However, most sequence numbers in computer protocols are limited to a fixed number of bits, and will wrap around after a sufficiently many numbers have been allocated. Thus, recently allocated serial numbers may duplicate very old serial numbers, but not other recently allocated serial numbers. To avoid ambiguity with these non-unique numbers, RFC&#; "Serial Number Arithmetic", defines special rules for calculations involving these kinds of serial numbers.
Lollipop sequence number spaces are a more recent and sophisticated scheme for dealing with finite-sized sequence numbers in protocols.
See also[edit]
Sources[edit]
- Elz, R., and R. Bush, RFC&#; "Serial Number Arithmetic", Network Working Group, August
- Plummer, William W. "Sequence Number Arithmetic". Cambridge, Massachusetts: Bolt Beranek and Newman, Inc., 21 September
References[edit]
External links[edit]
WeChat Revenue and Usage Statistics ()
WeChat was launched in as Weixin – Mandarin for ‘micro-message’. As the name suggests, it originally functioned as a simple messenger app – a Chinese equivalent to WhatsApp in essence.
WeChat was developed by and belongs to Chinese tech behemoth Tencent Holdings –one of the most valuable companies in the world, worth well over $ billion as of mid-May Other names in the Tencent stable include QQ Messenger and Riot Games (League of Legends), among many others. It also holds various significant stakes in other international app names (Snapchat, Fornite).
Tencent was reportedly on the brink of purchasing rival popular messaging app WhatsApp in An untimely surgery for CEO Pony Ma, however, allowed a panicked Mark Zuckerberg to get in there first.
Like WhatsApp, the WeChat app has evolved considerably since those early days. Video clips and functionality to find other users followed, then voice and video calls, as well as Facebook-like Moments feed. WeChat’s development would grow more innovative after this. Leveraging Tencent’s involvement in gaming (which would get deeper in years to come), it added games integration, and presciently, it also moved into the digital payments field – an area in which WhatsApp is trying to follow in the Indian market.
We can also add shopping, the ability to hail taxis (through Didi Chuxing), and mini brand apps run entirely within WeChat. In short, WeChat’s range of services give it the functionality of a whole suite of apps for its huge userbase. Indeed, its reach is so complete in China that it is even used to facilitate communication between judges and litigants in Chinese court cases and for the citizens of Guangzhou to store their ID cards.
Its userbase grew alongside functionality. Within 14 months it had climbed to million registered users; six months later this had doubled. Eventually the WeChat app overtook the then dominant Weibo, climbing today to over one billion monthly active users, a figure which continues to edge upwards. In , it crossed the same threshold for daily active users.
Sensor Tower's Q3 Mobile App Report
Get the latest Q3 app insights on the top performing apps and mobile games worldwide and by region, during COVID Also covered are mobile publisher and category performance with a special look at gaming genres, education apps, sports apps, and more.
Find out moreTencent’s ambitions lay beyond just China – hence the rebrand to the more internationally-friendly name ‘WeChat’. In , several new languages were added – mostly focused on SE Asia, though the inclusion of Portuguese suggested wider ambitions than China’s neighbours to the south. In markets where Facebook and Twitter are active – thus excluding the home market – users are able to sync up contacts from the aforementioned apps with WeChat, to encourage uptake.
This push was not considered to be a success – India, where WhatsApp remains the most-popular messaging app, is one high profile example – despite a concerted push into the still nascent market in , alongside several SE Asian markets. International user numbers were pegged at million in An impressive figure at the time, though the lack of any updated figure since suggests stagnation. Indeed, despite being one of the world&#;s biggest apps, many in the West could be forgiven for asking, &#;What is WeChat?&#;
The app has faced controversy over censorship issues – both within China and outside, with political content and ‘vulgar’ content blocked. Inevitably for an app in control of so much user data, there have been questions over data security also.
It remains, however, a central cog of Chinese digital life.
Below the contents, you&#;ll find some key WeChat user and revenue figures. Use the contents menu or scroll further down for more in-depth analysis, including more about WeChat user growth, the role it plays in multiple facets of society, and why it has Apple worried.
Table of Contents
WeChat Overview and Key Statistics
WeChat User Statistics
WeChat Usage Statistics
WeChat Revenue Statistics
WeChat Overview
| Launched | |
| HQ | Shenzen, China |
| People | Allen Zhang (founder, president of Weixin Group) |
| Parent company | Tencent |
| Company type | Public (SEHK, OTC:TCEHY) |
Key WeChat User Statistics
| WeChat MAU | |
| Q1 | 50 million |
| Q1 | million |
| Q1 | million |
| Q1 | million |
| Q1 | million |
| Q1 | million |
| Q1 | billion |
| Q1 | billion |
| Q2 | billion |
| Q3 | billion |
| Q4 | billion |
| Q1 | billion |
Source: We Are Social/Hootsuite and Statista
Key WeChat Usage Statistics
| Daily messages sent on WeChat | |
| ~Nov | ~30 billion |
| Nov | 38 billion |
| January | 45 billion |
Source: ZDNet and China Daily
| WeChat mini programs daily transactions | |
| January | 16 million |
| January | million |
| January | million |
| January | million |
| January | billion |
Source: Walk the Chat
Key WeChat Financial Statistics
| WeChat and other Tencent social media revenue | |
| FY | billion RMB* |
| FY | 37 billion RMB |
| FY | billion RMB |
| FY | billion RMB |
| Q1 | billion RMB |
| Q2 | billion RMB |
| Q3 | 22 billion RMB |
| Q4 | 22 billion RMB |
| FY | billion RMB |
*As of May , conversion rate of $1 = RMB; 1 RMB = $
Source: Tencent
| Tencent revenue | |
| FY | billion RMB* |
| FY | billion RMB |
| FY | billion RMB |
| FY | billion RMB |
| Q1 | billion RMB |
| Q2 | billion RMB |
| Q3 | billion RMB |
| Q4 | billion RMB |
| FY | billion RMB |
*As of May , conversion rate of $1 = RMB; 1 RMB = $
Source: Tencent
| Tencent operating profit/net profit | |
| FY | billion RMB/ billion RMB |
| FY | billion RMB/ billion RMB |
| FY | billion RMB/ billion RMB |
| FY | billion RMB/80 billion RMB |
| Q1 | billion RMB/ billion RMB |
| Q2 | billion RMB/ billion RMB |
| Q3 | billion RMB/21 billion RMB |
| Q4 | billion RMB/ billion RMB |
| FY | billion RMB/ billion RMB |
Source: Tencent
Other Key WeChat Statistics
- WeChat reported reaching 1 billion daily active users in January (ZDNet)
- WeChat the fifth most-used social app in the world (Hootsuite/We Are Social)
- WeChat penetration in China among year olds 78% in , down from 79% in ; second place Sino Weibo has 56% penetration (Hootsuite/We are Social)
- WeChat 10th most downloaded app in China as of January , falling from fifth in January (Hootsuite/We are Social)
- Over 20 million active ‘official accounts’ as of early (SCMP)
- In Q4 , WeChat reported a 15% year-on-year increase in daily messages sent increased (Tencent)
- million audio and video calls per day on WeChat (ZDNet)
- 46TB of data consumed on WeChat over one minute of the morning rush hour (TechNode)
- WeChat accounts for 34% of total mobile data traffic in China (Walk the Chat)
- Around 30% of mobile internet time in China is spent on WeChat (CNBC)
- 10 billion hits on WeChat Moments every 24 hours (TechNode)
- 83% of WeChat users use the general app for work (ChinaChannel)
- Average user had contacts (as of ) (ChinaChannel)
- , users use WeChat to access bus/metro services every minute during the morning rush hour (TechNode)
- Peak WeChat usage times at 9pm (official accounts) and 8pm (mini games) (China Internet Watch)
- Daily active WeChat mini programs users at over million in (China Internet Watch)
- WeChat mini program monthly active users at million in June (Walk the Chat)
- Average number of mini programs used per user doubled over , with usage increasing by 45% (China Internet Watch)
- WeChat mini programs generated billion RMB in , with average daily transaction volume doubling year-on-year (Walk the Chat)
- Over 1 million WeChat mini programs by the end of (TechCrunch)
- 2, mini games available on WeChat (WeChat)
- million monthly players of WeChat mini games (The Verge)
- Mini games account for 33% of the top mini programs, with 81% of mini programs users playing a game (China Internet Watch)
- Top WeChat mini games played by over million users (Walk the Chat)
- As of mid-March, million WeChat users used WeChat Health to access pandemic updates, online consultation, and AI-powered self-assessment related to the coronavirus outbreak (Tencent)
- At the same point, coronavirus-related content had attracted million page views on WeChat and Tencent News (Tencent)
- WeChat Moments counts million daily users (TechNode)
- WeChat Work used by million companies and 60 million MAU (China Internet Watch)
- million users of WeChat Pay on a monthly basis in Q4 (Tencent)
- Tenpay market penetration at 84% (includes other Tencent payment apps), though AliPay has a higher transaction volume (Dragon Social)
- Tenpay claimed % market share of third-party payments in Q3 (iResearch)
- 1 billion WeChat Pay commercial transactions per day in Q4 (Walk the Chat)
- 72 million businesses registered to WeChat Pay in (China Internet Watch)
- 50 million monthly active merchants on WeChat Pay in Q4 (Tencent)
- million users of credit rating system WeChat Payments Score within a year of launch (China Internet Watch)
- 8 billion visits to WeChat visits by users getting ‘health codes’ needed to travel around China during the coronavirus pandemic, as of mid-March (Reuters)
- million users sent or received a Chinese New Year red package over WeChat in (Straits Times)
- WeChat drove $50 billion into the Chinese economy in (CAICT)
- Tencent social network revenue totalled billion RMB in
- Total Tencent revenue for was billion RMB
- As of Q4 , 21% of Tencent revenue was generated through its social networks (WeChat, QQ, and Qzone), with the total figure for at 23% (Tencent)
- Tencent market cap $ billion in mid-May (Yahoo Finance)
WeChat User Statistics
WeChat counts over one billion active monthly users – a threshold it crossed in the early days of As of Q4 , it counted billion users in all. In a league table of the world&#;s most popular messaging apps, this puts it behind only WhatsApp and Facebook Messenger, and fifth overall in terms of social media.
The fastest levels of growth were seen between and Growth over seems to have slowed, though not to a great degree. This is perhaps to be expected. Thought its userbase extends beyond the domestic market, as it stands, WeChat’s market penetration is close to complete in China, as detailed below (counting international users, the WeChat monthly active user count is only million fewer than the entire population of China, it should be noted).
It is hard, therefore, to see how much more it could possibly grow without a more successful international push than we have seen to date. At present, We Are Social/Hootsuite find that WeChat is only the number one in app in three countries (this does not include Taiwan or Hong Kong – according to AppAnnie it is the sixth-most and second-most downloaded app in these territories, and the sixth-most and the third-most used app, respectively; it is the fifth-most used, and 10th-most used in Malaysia).
Some estimates peg international WeChat user numbers at between and million. Tencent is reticent to provide user numbers broken down by country.
WeChat monthly active users, million
Data source: Statista and Tencent
Tencent announced in early that daily active users of the app had climbed to over one billion. To out that a little into perspective, that’s roughly the population of the entire Americas. Only Facebook, YouTube, WhatsApp, and Facebook Messenger can claim a greater share of global usage.
It might be worth noting that the numbers WeChat publicise tend to refer to accounts rather than users. One user might use multiple accounts – though that does not greatly detract from the heft of these numbers.
In a chart published in July , eMarketer pegged mainland Chinese monthly active WeChat smartphone user numbers at a little under million They predicted the number of Chinese WeChat users would rise to million by
The real story, however, comes from the stunning penetration levels – already at a stratospheric 79% of smartphone users, and 85% of messaging app users, eMarketer predicts we’ll see any even higher levels of 82% of smartphone users and 89% of messaging app users in the years to come.
Projected user growth and penetration in China
Source: eMarketer
In January , Hootsuite and We Are Social put the current number of mobile social media users in China at billion, with mobile subscriptions at over billion, outstripping the country’s total population (97% of the population have mobile phones, and 83% smart phones). The former represents a 10% increase over , the latter 9% – showing that despite high levels of penetration, the market continues to grow apace.
The same report also pegs current WeChat app penetration at 79%, with Baidu Tieba (72%) and QQ (68%) not a huge distance behind.
Chinese social app penetration
Source: We Are Social/Hootsuite
WeChat does not top the most-downloaded list, however – quite possibly because it has been downloaded and installed so many times already. In January , it lagged in fifth place, with the global app of , TikTok, topping the chart. As of April , it had fallen to 10th.
Top Chinese mobile apps by downloads
Source: We Are Social/Hootsuite
WeChat does not make the top mobile apps in terms of revenue, with stablemate Tencent Video in front here. QQ an QQ Music also make the list, as does karaoke app WeSing (finding precise revenue for many of these apps can be difficult it must be noted, with Tencent not tending to release by-app data – the source here uses App Store and Google Play data).
Speaking to the FT, Matthew Brennan, the founder of consultancy ChinaChannel (which focuses on WeChat), posited that much of the growth that we’ve seen over the past couple of years came from international markets – chiefly in Southeast Asia, Europe, and the US. Much, he continues, is likely to be from the Chinese diaspora aiming to stay in touch with those back home.
Further international growth is unlikely to be fostered by news such as the University of California warning its students not to use WeChat in China, as posts could be used against them by edgy authorities.
WeChat demographics
Peak WeChat usage occurs in the age group, as tends to be the case across messenger apps. It has not been ever this; there has been a shift over time – with the peak shifting from the previously dominant to the group between and
We also see a higher proportion of older age groups using WeChat, across the board. Notably the proportion of users in the age bracket is not too far off matching year olds.
Age distribution of WeChat users
Source: Walk the Chat
It’s certainly not all about young users. Indeed, 63 million WeChat users above the age of 55 open their account at least once a month (in all China has million citizens over the age of 60 – or 17% of the total population).
Indeed, if we look to the proportion of data usage accounted for by the WeChat app, we might note that the older users get, the greater the proportion of data usage expended on WeChat.
Of the 60+ WeChat demographic, for instance, 61% of users reportedly use more than half of their total mobile data usage on WeChat. For year olds, the proportion is a shade under 50%. On the other hand, only 14% of unders, or 25% of s can report the same.
WeChat clearly serves a more prominent part in the lives of older users than other apps favoured by younger ones.
WeChat data usage by age group
Source: Walk the Chat
WeChat statistics covering the Chinese New Year period in found that those born in the 90s accounted for the greatest share of messages.
Chinese New Year age demographics of active WeChat users
Data source: ChinaChannel
Older users are reportedly keen users of ‘mini programs’ – that is native mini apps that can be built within WeChat.
Tencent themselves carried out study alongside a team from Shenzen University to measure usage levels among the over demographic, surveying over 3, older Chinese citizens, only 10% of whom came from top-tier (i.e. the most populous cities).
In the below chart, the top row shows age, the middle earnings in RMB (2, RMB is about $), and the last educational attainment. We see familiar app trends here, as usage levels are more concentrated in those who are younger, earn more money, and are educated to a higher level.
WeChat usage levels among over 55s
Source: TechNode
What’s notable here is, given the age group in question, is how high the usage levels reported are. Above 50% for the unders, and not quite tailing off until we get to the over 80 demographic – where usage levels are still relatively high, considering.
We see even the lowest income bracket reporting usage of a third. Perhaps the most significant factor is educational attainment, where those who were not able to pursue anything beyond compulsory education report notably low usage levels, compared to those who are able to progress further.
Without the ability to cross reference this with other indicators, we can only speculate, but it may be that this less-educated bracket is roughly analogous with the oldest age bracket – with opportunities for and commitment to higher education progressively increasing in China over the decades.
The study found that on average these users had WeChat friends, 23% of whom were family members, showing the benefits of social connectivity in maintaining a wider contact network for older people – indeed the study found (perhaps predictably, given it was commissioned), that WeChat was connected with the happiness of older users.
It’s not just about staying in touch with family and friends, however; 75% of WeChat users over the age of 55 read subscription articles, 62% share them, and over 50% used payment app WeChat Pay. This market of silver surfers can clearly be of value to brands and media outlets, then.
WeChat released figures revealing users’ favourite emoji by age group. Younger users, born this side of the millennium, most-frequently use the ‘facepalm’. ‘Crying with laughter’ tops the list for 90s kids, a grin for those born in the s, which develops into a fully-fledged chuckle for users born in the 70s. Overs most-commonly use the thumbs-up.
The revelation of these preferred emojis led to concerns over levels of data security.
Finding up-to-date information on WeChat demographics can be a tricky task. The latest reliable gender breakdown of WeChat app users seems to date back to – since when we know a lot of developments have occurred.
These figures show a ratio of nearly in favour of male users – perhaps to be expected in China’s rather patriarchal society, though perhaps not tarrying with the app&#;s ubiquity.
WeChat users by gender
Source: TechNode
The largest share of WeChat users are employed in the private sector or are self-employed/freelance, according to the same study. These two sectors alone account for around 60% of users (or did at the time). Students and public sector workers accounted for another 30%.
WeChat users by occupation
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