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#Discussion
## Errors and Biases
When compared to major polling companies and their ongoing polls published in the media, it seems that several parties are under-represented while others are over-represented, even after introducing our statistical controls.
Some notable examples are that according to our prediction:
- The left-wing party Meretz gets 9 seats (4-5 seats in other major polls).
- The extreme-right-wing party Yahad gets 9 seats (4-5 in other major polls).
- The separadic-orthodox party Shas gets 0 seats (8 in other major polls)
- The right-wing party Israel-Beytenu gets 0 seats (5 in other major polls)
One possible source of bias is the influence of abstention (non-voting). Our model does not incorporate a mechanism to assess changes in turn-out. While we did ask our respondents whether they abstained in 2013, it would be naive to assume that they represent the non-voting population. A non voter is presumably indifferent and would not participate in our poll. Those who do participate, probably intend to vote in 2015. We could therefore easily reach the false conclusion that turn-out will increase to nearly 100% giving those users who reported abstention in 2013 unreasonably high weights. Eventually we chose to ignore possible changes in abstention, implicitly assuming that the voting population is constant and that we need only to infer if and how they will change their vote. In particular, there are media reports that turn-out will increase dramatically in the Arab population, which could increase the number of seats for the Arab Union.
Other possible sources of bias are several demographic variables that we were not controlling for (see section 2.2). Since we are already controlling for the 2013 election results (_i.e._ our weighted sample 'agrees' with the actual 2013 results), the following question is of relevance to the sources of bias:
In what way are our respondents that voted for party i in 2013 different then the actual voting population that voted for party i in 2013. Some variables that may have played a role in biasing our sample could be:
- Age. It is not unreasonable to assume that young voters are more likely to vote for new, small, niche or extreme parties than are older voters. For example, older voters who voted for Likud in 2013 are more likely to stick with Likud than are youngers voters, who are in turn more likely to switch to Yahad or Kulanu. This same bias could explain the high number of seats projected for Meretz and the low number of seats projected for Israel Beytenu.
- Sex. It is possible that females changed their minds (between 2013 and 2015) in a manner different then men. Ha'Mahane Ha'Zioni has a lot of women in their list, including Livni who is set to become prime minister through rotation with Herzog. As an example, females who voted for Yesh Atid in 2013 just might be more likely to switch to Ha'Mahane Ha'Zioni than men who voted for Yesh Atid in 2013.
Another source of error could be the sample size. 2013 votes were only collected from respondents after February 13th, and only in Android devices. Therefore, the 2013 dataset contains only ~2400 votes, roughly a third of our entire sample. Due to our methodology, it is imperative to have a reasonable size sample for each voter 'weight class', which is decided in our case by the voter's 2013 vote. For instance, we might have 400 respondents who voted Ha'Likud in 2013, but only 5 that voted Shas. Those few respondents in the 2013-Shas weight class will get very high weights, leading to very large error margins. Due to time limitations, we did not have the time to assess these error margins, so it is quite likely that they explain a lot of the discrepancies we're seeing. In particular, this could explain the low number of seats projected for Shas.
