IoT and Embedded Systems BlackBerry Radar Support BlackBerry Certicom Support BlackBerry QNX Support BlackBerry QNX Developer Network BlackBerry QNX Training. Secure Communications BBM Enterprise BlackBerry AtHoc. Product Lifecycle BlackBerry Lifecycle Legacy Smartphone Devices. Additional Resources and Portals. Stay up to date with the latest developments and connect with other users.

Help Blog Get the latest news about product updates. Developer Network For developers using BlackBerry SDKs, APIs, and development tools. Example: We want to estimate the total income of adults living in a given street.

We visit each household in that street, identify all adults living there, and randomly select one adult from each household. For example, we can allocate each person a random number, generated from a uniform distribution between 0 and 1, and select the person with the highest number in each household.

We then interview the selected person and find their income. People living on their own are certain to be selected, so we simply add their income to our estimate of the total. But a person living in a household of two adults has only a one-in-two chance of selection. To reflect this, when we come to such a household, we would count the selected person's income twice towards the total.

The person who is selected from that household can be loosely viewed as also representing the person who isn't selected. In the above example, not everybody has the same probability of selection; what makes it a probability sample is the fact that each person's probability is known.

When every element in the population does have the same probability of selection, this is known as an 'equal probability of selection' EPS design. Such designs are also referred to as 'self-weighting' because all sampled units are given the same weight.

Probability sampling includes: Simple Random Sampling , Systematic Sampling , Stratified Sampling , Probability Proportional to Size Sampling, and Cluster or Multistage Sampling. These various ways of probability sampling have two things in common:. It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection. Hence, because the selection of elements is nonrandom, nonprobability sampling does not allow the estimation of sampling errors.

These conditions give rise to exclusion bias , placing limits on how much information a sample can provide about the population. Information about the relationship between sample and population is limited, making it difficult to extrapolate from the sample to the population.

Example: We visit every household in a given street, and interview the first person to answer the door. In any household with more than one occupant, this is a nonprobability sample, because some people are more likely to answer the door e.

an unemployed person who spends most of their time at home is more likely to answer than an employed housemate who might be at work when the interviewer calls and it's not practical to calculate these probabilities. Nonprobability sampling methods include convenience sampling , quota sampling , and purposive sampling. In addition, nonresponse effects may turn any probability design into a nonprobability design if the characteristics of nonresponse are not well understood, since nonresponse effectively modifies each element's probability of being sampled.

Within any of the types of frames identified above, a variety of sampling methods can be employed individually or in combination. Factors commonly influencing the choice between these designs include:. In a simple random sample SRS of a given size, all subsets of a sampling frame have an equal probability of being selected. Each element of the frame thus has an equal probability of selection: the frame is not subdivided or partitioned.

Furthermore, any given pair of elements has the same chance of selection as any other such pair and similarly for triples, and so on. This minimizes bias and simplifies analysis of results. In particular, the variance between individual results within the sample is a good indicator of variance in the overall population, which makes it relatively easy to estimate the accuracy of results.

Simple random sampling can be vulnerable to sampling error because the randomness of the selection may result in a sample that doesn't reflect the makeup of the population.

For instance, a simple random sample of ten people from a given country will on average produce five men and five women, but any given trial is likely to over represent one sex and underrepresent the other. Systematic and stratified techniques attempt to overcome this problem by "using information about the population" to choose a more "representative" sample. Also, simple random sampling can be cumbersome and tedious when sampling from a large target population.

In some cases, investigators are interested in research questions specific to subgroups of the population. For example, researchers might be interested in examining whether cognitive ability as a predictor of job performance is equally applicable across racial groups. Simple random sampling cannot accommodate the needs of researchers in this situation, because it does not provide subsamples of the population, and other sampling strategies, such as stratified sampling, can be used instead.

Systematic sampling also known as interval sampling relies on arranging the study population according to some ordering scheme and then selecting elements at regular intervals through that ordered list. Systematic sampling involves a random start and then proceeds with the selection of every k th element from then onwards.

It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first to the k th element in the list. A simple example would be to select every 10th name from the telephone directory an 'every 10th' sample, also referred to as 'sampling with a skip of 10'. As long as the starting point is randomized , systematic sampling is a type of probability sampling.

It is easy to implement and the stratification induced can make it efficient, if the variable by which the list is ordered is correlated with the variable of interest. For example, suppose we wish to sample people from a long street that starts in a poor area house No. A simple random selection of addresses from this street could easily end up with too many from the high end and too few from the low end or vice versa , leading to an unrepresentative sample.

Selecting e. every 10th street number along the street ensures that the sample is spread evenly along the length of the street, representing all of these districts. Note that if we always start at house 1 and end at , the sample is slightly biased towards the low end; by randomly selecting the start between 1 and 10, this bias is eliminated. However, systematic sampling is especially vulnerable to periodicities in the list. If periodicity is present and the period is a multiple or factor of the interval used, the sample is especially likely to be un representative of the overall population, making the scheme less accurate than simple random sampling.

For example, consider a street where the odd-numbered houses are all on the north expensive side of the road, and the even-numbered houses are all on the south cheap side. Under the sampling scheme given above, it is impossible to get a representative sample; either the houses sampled will all be from the odd-numbered, expensive side, or they will all be from the even-numbered, cheap side, unless the researcher has previous knowledge of this bias and avoids it by a using a skip which ensures jumping between the two sides any odd-numbered skip.

Another drawback of systematic sampling is that even in scenarios where it is more accurate than SRS, its theoretical properties make it difficult to quantify that accuracy. In the two examples of systematic sampling that are given above, much of the potential sampling error is due to variation between neighbouring houses — but because this method never selects two neighbouring houses, the sample will not give us any information on that variation.

As described above, systematic sampling is an EPS method, because all elements have the same probability of selection in the example given, one in ten. It is not 'simple random sampling' because different subsets of the same size have different selection probabilities — e. the set {4,14,24, Systematic sampling can also be adapted to a non-EPS approach; for an example, see discussion of PPS samples below.

When the population embraces a number of distinct categories, the frame can be organized by these categories into separate "strata. First, dividing the population into distinct, independent strata can enable researchers to draw inferences about specific subgroups that may be lost in a more generalized random sample. Second, utilizing a stratified sampling method can lead to more efficient statistical estimates provided that strata are selected based upon relevance to the criterion in question, instead of availability of the samples.

Even if a stratified sampling approach does not lead to increased statistical efficiency, such a tactic will not result in less efficiency than would simple random sampling, provided that each stratum is proportional to the group's size in the population.

Third, it is sometimes the case that data are more readily available for individual, pre-existing strata within a population than for the overall population; in such cases, using a stratified sampling approach may be more convenient than aggregating data across groups though this may potentially be at odds with the previously noted importance of utilizing criterion-relevant strata.

Finally, since each stratum is treated as an independent population, different sampling approaches can be applied to different strata, potentially enabling researchers to use the approach best suited or most cost-effective for each identified subgroup within the population. There are, however, some potential drawbacks to using stratified sampling. First, identifying strata and implementing such an approach can increase the cost and complexity of sample selection, as well as leading to increased complexity of population estimates.

Second, when examining multiple criteria, stratifying variables may be related to some, but not to others, further complicating the design, and potentially reducing the utility of the strata. Finally, in some cases such as designs with a large number of strata, or those with a specified minimum sample size per group , stratified sampling can potentially require a larger sample than would other methods although in most cases, the required sample size would be no larger than would be required for simple random sampling.

Stratification is sometimes introduced after the sampling phase in a process called "poststratification". Although the method is susceptible to the pitfalls of post hoc approaches, it can provide several benefits in the right situation. Implementation usually follows a simple random sample. In addition to allowing for stratification on an ancillary variable, poststratification can be used to implement weighting, which can improve the precision of a sample's estimates.

Choice-based sampling is one of the stratified sampling strategies. In choice-based sampling, [8] the data are stratified on the target and a sample is taken from each stratum so that the rare target class will be more represented in the sample.

The model is then built on this biased sample. The effects of the input variables on the target are often estimated with more precision with the choice-based sample even when a smaller overall sample size is taken, compared to a random sample. The results usually must be adjusted to correct for the oversampling.

In some cases the sample designer has access to an "auxiliary variable" or "size measure", believed to be correlated to the variable of interest, for each element in the population. These data can be used to improve accuracy in sample design.

One option is to use the auxiliary variable as a basis for stratification, as discussed above. Another option is probability proportional to size 'PPS' sampling, in which the selection probability for each element is set to be proportional to its size measure, up to a maximum of 1. In a simple PPS design, these selection probabilities can then be used as the basis for Poisson sampling.

However, this has the drawback of variable sample size, and different portions of the population may still be over- or under-represented due to chance variation in selections.

Systematic sampling theory can be used to create a probability proportionate to size sample. This is done by treating each count within the size variable as a single sampling unit.

Samples are then identified by selecting at even intervals among these counts within the size variable. This method is sometimes called PPS-sequential or monetary unit sampling in the case of audits or forensic sampling. Example: Suppose we have six schools with populations of , , , , , and students respectively total students , and we want to use student population as the basis for a PPS sample of size three. If our random start was , we would select the schools which have been allocated numbers , , and , i.

the first, fourth, and sixth schools. The PPS approach can improve accuracy for a given sample size by concentrating sample on large elements that have the greatest impact on population estimates. PPS sampling is commonly used for surveys of businesses, where element size varies greatly and auxiliary information is often available — for instance, a survey attempting to measure the number of guest-nights spent in hotels might use each hotel's number of rooms as an auxiliary variable.

In some cases, an older measurement of the variable of interest can be used as an auxiliary variable when attempting to produce more current estimates. Sometimes it is more cost-effective to select respondents in groups 'clusters'. Sampling is often clustered by geography, or by time periods. Nearly all samples are in some sense 'clustered' in time — although this is rarely taken into account in the analysis.

For instance, if surveying households within a city, we might choose to select city blocks and then interview every household within the selected blocks. Clustering can reduce travel and administrative costs. In the example above, an interviewer can make a single trip to visit several households in one block, rather than having to drive to a different block for each household.

It also means that one does not need a sampling frame listing all elements in the target population. Instead, clusters can be chosen from a cluster-level frame, with an element-level frame created only for the selected clusters.

In the example above, the sample only requires a block-level city map for initial selections, and then a household-level map of the selected blocks, rather than a household-level map of the whole city. Cluster sampling also known as clustered sampling generally increases the variability of sample estimates above that of simple random sampling, depending on how the clusters differ between one another as compared to the within-cluster variation.

For this reason, cluster sampling requires a larger sample than SRS to achieve the same level of accuracy — but cost savings from clustering might still make this a cheaper option. Cluster sampling is commonly implemented as multistage sampling. This is a complex form of cluster sampling in which two or more levels of units are embedded one in the other. The first stage consists of constructing the clusters that will be used to sample from. In the second stage, a sample of primary units is randomly selected from each cluster rather than using all units contained in all selected clusters.

In following stages, in each of those selected clusters, additional samples of units are selected, and so on. All ultimate units individuals, for instance selected at the last step of this procedure are then surveyed. This technique, thus, is essentially the process of taking random subsamples of preceding random samples.

Multistage sampling can substantially reduce sampling costs, where the complete population list would need to be constructed before other sampling methods could be applied.

By eliminating the work involved in describing clusters that are not selected, multistage sampling can reduce the large costs associated with traditional cluster sampling. In quota sampling , the population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then judgement is used to select the subjects or units from each segment based on a specified proportion.

For example, an interviewer may be told to sample females and males between the age of 45 and It is this second step which makes the technique one of non-probability sampling. In quota sampling the selection of the sample is non- random. For example, interviewers might be tempted to interview those who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection.

This random element is its greatest weakness and quota versus probability has been a matter of controversy for several years. Why, ones like the "agreement between Activision Blizzard and Sony," that places "restrictions on the ability of Activision Blizzard to place COD titles on Game Pass for a number of years". It was apparently these kinds of agreements that Xbox's Phil Spencer had in mind opens in new tab when he spoke to Sony bosses in January and confirmed Microsoft's "intent to honor all existing agreements upon acquisition of Activision Blizzard".

Unfortunately, the footnote ends there, so there's not much in the way of detail about what these restrictions are or how long they'd remain in effect in a potential post-acquisition world. Given COD's continued non-appearance on Game Pass, you've got to imagine the restrictions are fairly significant if they're not an outright block on COD coming to the service. Either way, the simple fact that Microsoft is apparently willing to maintain any restrictions on its own ability to put first-party games on Game Pass is rather remarkable, given that making Game Pass more appealing is one of the reasons for its acquisition spree.

The irony of Sony making deals like this one while fretting about COD's future on PlayStation probably isn't lost on Microsoft's lawyers, which is no doubt part of why they brought it up to the CMA. While it's absolutely reasonable to worry about a world in which more and more properties are concentrated in the hands of singular, giant megacorps, it does look a bit odd if you're complaining about losing access to games while stopping them from joining competing services.

We'll find out if the CMA agrees when it completes its in-depth, "Phase 2" investigation opens in new tab into the Activision Blizzard acquisition, which is some way off yet. For now, we'll have to content ourselves with poring over these kinds of corporate submissions for more interesting tidbits like this one. So far, we've already learned that Microsoft privately has a gloomy forecast for the future of cloud gaming opens in new tab , and that the company thinks Sony shouldn't worry so much since, hey, future COD games might be as underwhelming as Vanguard opens in new tab.

A footnote in Microsoft's submission opens in new tab to the UK's Competition and Markets Authority CMA has let slip the reason behind Call of Duty's absence from the Xbox Game Pass library: Sony and Activision Blizzard have a deal that restricts the games' presence on the service.

The footnote appears in a section detailing the potential benefits to consumers from Microsoft's point of view of the Activision Blizzard catalogue coming to Game Pass. What existing contractual obligations are those? Why, ones like the "agreement between Activision Blizzard and Sony," that places "restrictions on the ability of Activision Blizzard to place COD titles on Game Pass for a number of years". It was apparently these kinds of agreements that Xbox's Phil Spencer had in mind opens in new tab when he spoke to Sony bosses in January and confirmed Microsoft's "intent to honor all existing agreements upon acquisition of Activision Blizzard".

Unfortunately, the footnote ends there, so there's not much in the way of detail about what these restrictions are or how long they'd remain in effect in a potential post-acquisition world.

Given COD's continued non-appearance on Game Pass, you've got to imagine the restrictions are fairly significant if they're not an outright block on COD coming to the service. Either way, the simple fact that Microsoft is apparently willing to maintain any restrictions on its own ability to put first-party games on Game Pass is rather remarkable, given that making Game Pass more appealing is one of the reasons for its acquisition spree.

The irony of Sony making deals like this one while fretting about COD's future on PlayStation probably isn't lost on Microsoft's lawyers, which is no doubt part of why they brought it up to the CMA. While it's absolutely reasonable to worry about a world in which more and more properties are concentrated in the hands of singular, giant megacorps, it does look a bit odd if you're complaining about losing access to games while stopping them from joining competing services.

We'll find out if the CMA agrees when it completes its in-depth, "Phase 2" investigation opens in new tab into the Activision Blizzard acquisition, which is some way off yet. For now, we'll have to content ourselves with poring over these kinds of corporate submissions for more interesting tidbits like this one.

So far, we've already learned that Microsoft privately has a gloomy forecast for the future of cloud gaming opens in new tab , and that the company thinks Sony shouldn't worry so much since, hey, future COD games might be as underwhelming as Vanguard opens in new tab.

Who knows what we'll learn next? Sign up to get the best content of the week, and great gaming deals, as picked by the editors. One of Josh's first memories is of playing Quake 2 on the family computer when he was much too young to be doing that, and he's been irreparably game-brained ever since.

His writing has been featured in Vice, Fanbyte, and the Financial Times. He'll play pretty much anything, and has written far too much on everything from visual novels to Assassin's Creed. His most profound loves are for CRPGs, immersive sims, and any game whose ambition outstrips its budget.

He thinks you're all far too mean about Deus Ex: Invisible War. Open menu Close menu PC Gamer PC Gamer THE GLOBAL AUTHORITY ON PC GAMES. opens in new tab opens in new tab opens in new tab opens in new tab opens in new tab opens in new tab. US Edition. News Reviews Hardware Best Of Magazine The Top Forum More PCGaming Show Podcasts Coupons Newsletter SignUp Community Guidelines Affiliate Links Meet the team About PC Gamer.

Popular WoW: Dragonflight Darktide Midnight Suns Holiday gifts Warzone 2. Audio player loading…. PC Gamer Newsletter Sign up to get the best content of the week, and great gaming deals, as picked by the editors. Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors. Joshua Wolens. See comments.

Latest Video. Fox News host Dan Bongino discusses the crackdown on conservative censorship in Saturday's 'Unfiltered' monologue. By Jeremy Jenkins. News Reviews Hardware Best Of Magazine The Top Forum More PCGaming Show Podcasts Coupons Newsletter SignUp Community Guidelines Affiliate Links Meet the team About PC Gamer. On June 6, , the U. Nearly all samples are in some sense 'clustered' in time — although this is rarely taken into account in the analysis.

Categories:

• Forex Brokers
• Binary Options
• Forex Bitcoin
• Forex
• Binary Options online